Manajemen Penilaian Risiko Keuangan pada Organisasi
Modern: Suatu Tinjauan Literatur Sistematis tentang Metode dan Pendekatan
Manajemen Risiko
Financial Risk Assessment Management in Modern
Organizations: A Systematic Literature Review of Risk Management Methods and
Approaches
Desi Rosalina 1,2 , Nera Marinda Machdar 3
, Adler Haymans Manurung 4
Bhayangkara University, Greater Jakarta, Indonesia 1,3,4
Adzkia University, Padang, Indonesia 2
202530151001@mhs.ubharajaya.ac.id
desirosalina@adzkia.ac.id
Abstrak
Globalisasi,
transformasi digital, dan meningkatnya ketidakpastian ekonomi menyebabkan
organisasi menghadapi risiko keuangan yang semakin kompleks. Kondisi tersebut
menegaskan pentingnya penerapan Manajemen Penilaian Risiko Keuangan sebagai
bagian dari pengambilan keputusan strategis dan manajemen risiko organisasi.
Penelitian ini bertujuan menganalisis perkembangan metode, implementasi, tren
penelitian, serta faktor-faktor yang memengaruhi Manajemen Penilaian Risiko
Keuangan pada organisasi modern. Penelitian menggunakan metode Systematic
Literature Review (SLR) dengan pendekatan PRISMA 2020. Data diperoleh dari
basis data Scopus menggunakan kata kunci financial risk assessment dan enterprise
risk management. Proses identifikasi menghasilkan 79 artikel, kemudian
melalui tahap penyaringan dan evaluasi hingga diperoleh 25 artikel
internasional yang memenuhi kriteria penelitian.
Hasil
penelitian menunjukkan bahwa publikasi mengenai Financial Risk Assessment
Management meningkat secara signifikan dalam sepuluh tahun terakhir, khususnya
setelah tahun 2018. Penelitian didominasi oleh metode kuantitatif (50%) dan
eksperimen berbasis model (42,3%), dengan objek penelitian yang sebagian besar
berasal dari sektor bisnis dan industri (84%). Selain itu, penelitian
menunjukkan adanya pergeseran dari metode tradisional seperti Analytic
Hierarchy Process (AHP), Fuzzy Logic, dan Monte Carlo Simulation menuju
pendekatan berbasis teknologi seperti machine learning, deep learning,
artificial intelligence, text mining, graph neural networks, dan predictive
analytics. Implementasi penilaian risiko keuangan juga telah berkembang pada
berbagai sektor seperti kesehatan, pariwisata, rantai pasok, energi terbarukan,
keuangan digital, dan perusahaan milik negara.
Penelitian
ini menyimpulkan bahwa kualitas data, metode analisis, teknologi digital,
karakteristik industri, ketidakpastian lingkungan bisnis, serta aspek
keberlanjutan merupakan faktor utama yang memengaruhi efektivitas Manajemen
Penilaian Risiko Keuangan. Oleh karena itu, organisasi perlu mengembangkan
sistem penilaian risiko yang lebih terintegrasi, adaptif, dan berbasis data
guna meningkatkan kualitas pengambilan keputusan dan mendukung keberlanjutan
bisnis di masa depan.
Kata
Kunci: Manajemen Penilaian
Risiko Keuangan, Penilaian Risiko Keuangan, Manajemen Risiko Perusahaan,
Manajemen Risiko, Tinjauan Literatur Sistematis.
Abstract
Globalization,
digital transformation, and increasing economic uncertainty have caused
organizations to face increasingly complex financial risks. These conditions
emphasize the importance of implementing Financial Risk Assessment Management
as part of strategic decision-making and organizational risk management. This
study aims to analyze the development of methods, implementation, research
trends, and factors influencing Financial Risk Assessment Management in modern
organizations. The method used was a Systematic Literature Review (SLR) with
the PRISMA 2020 approach. Data sources were sourced from the Scopus database
using the keywords "financial risk assessment" and "enterprise
risk management." The identification process yielded 79 articles, which
were then screened, assessed for eligibility, and included, resulting in 25
international articles meeting the research criteria.
The
results show that publications on Financial Risk Assessment Management have
increased significantly over the past ten years, particularly after 2018.
Research is dominated by quantitative methods (50%) and model experiments
(42.3%), while the research subjects largely focused on the business and
industrial sectors (84%). The findings also indicate a shift from traditional
methods, such as the Analytic Hierarchy Process (AHP), Fuzzy Logic, and Monte
Carlo Simulation, to technology-based approaches, including machine learning,
deep learning, artificial intelligence, text mining, graph neural networks, and
predictive analytics. Furthermore, the implementation of financial risk
assessment has expanded across various sectors, including healthcare, tourism, supply
chains, renewable energy, digital finance, and state-owned enterprises.
This
study concludes that data quality, analysis methods, digital technology,
industry characteristics, business environment uncertainty, and sustainability
aspects are key factors influencing the effectiveness of Financial Risk
Assessment Management. Therefore, organizations need to develop more
integrated, adaptive, and data-driven risk assessment systems to improve
decision-making quality and support future business sustainability.
Keywords: Financial Risk Assessment, Financial Risk Assessment
Management, Enterprise Risk Management, Risk Management, Systematic Literature
Review.
INTRODUCTION
Globalization, digital transformation, and global
economic uncertainty are creating increasingly complex and dynamic financial
risks for organizations. Changing market conditions, developments in financial
technology, increased business competition, and economic instability are
driving companies to strengthen their Financial Risk Assessment Management
systems as a crucial component of strategic organizational decision-making (Li et al., 2018) (Franzoni et al., 2019) (Zhao, 2024). Financial risks not only impact a company's financial
stability but also impact business sustainability, operational effectiveness,
organizational reputation, and even investor confidence (İşletmelerinde &
Risklerin, 2021) (Fan et al., 2023).
Financial risk assessment is the process of identifying,
measuring, analyzing, and controlling financial risks to help organizations
minimize potential losses and improve the quality of managerial
decision-making. Over time, financial risk assessment has transformed from a
traditional approach to a more systematic, integrated, and quantitative
modeling-based approach (Kumar et al., 2017)(Li et al., 2018) explain that
using heterogeneous information and historical aggregated data can
improve the quality of a company's risk predictions. Furthermore, research by (Brunner-kirchmair, 2019) confirms that collaborative financial risk assessment is
a crucial part of modern organizations because risk management requires the
involvement of various stakeholders in the decision-making process.
In practice, financial risk assessment has been applied
across various industrial sectors. In the healthcare sector,(Kondoju & Bindewari,
2025) explain that healthcare organizations require an enterprise
risk management system to control procurement risk, liquidity
risk, and operational financial risk to maintain organizational
stability. In the tourism sector, (Su & Zhou, 2018) show that tourism companies face significant financial
risks due to market changes and industry uncertainty, thereby requiring a risk
evaluation system that supports business sustainability. Furthermore, Franzoni
and Pelizzari (2019) explain that external factors, such as changes in weather
and rainfall, can also affect the profitability of hospitality companies,
requiring a scenario-based financial risk assessment system to mitigate
the impact of these risks on business performance.
The development of financial risk assessment is also
evident in the maritime and logistics sectors. Research on the Financial
Risk Assessment of Marine Enterprises Based on the Analytic Hierarchy Process shows
that the Analytic Hierarchy Process (AHP) method can help companies
evaluate risks through systematic risk indicator weighting. Furthermore,
research on the Financial Risk Assessment and Pre-warning of Port
Enterprises Based on a Neural Network Model indicates that pre-warning and
early warning systems can help port companies detect potential financial
distress more quickly, thereby enabling more effective risk management
decisions.
Digital transformation and the development of internet
finance have increased the complexity of financial risks in modern
organizations. (Yan & Wang, 2020) explain that
internet finance carries high risks in operational risk, credit risk,
legal risk, and internet security risk, thus requiring a more
adaptive risk management approach. Research by (Wang, 2025) also shows that blockchain and fuzzy neural networks can
be used to increase transparency and effectiveness in risk management in supply
chain finance. Furthermore,(Zhao, 2024) explains that the use of spillover indexes and VAR
models can help organizations understand the relationships among financial
markets and control spillover risks in modern corporate financial systems.
Besides its application in the organizational and
financial services sectors, financial risk assessment is also developing in the
fields of supply chain finance and green finance. Research by (Wang, 2025) shows that integrating multiple information sources can
improve the quality of credit risk evaluations in green supply chain
finance for small and medium-sized enterprises. Other research explains the
use of graph attention neural networks to improve the effectiveness of
risk evaluation in supply chain finance by adopting a more complex, dynamic
relational modeling approach. This demonstrates that modern financial risk
management is increasingly moving towards integrated, data-driven systems.
The development of financial risk assessment methods also
demonstrates the growing use of quantitative modeling in organizational
decision-making. (Kumar et al., 2017) explain that Monte Carlo Simulation and NPV-at-Risk
are used to evaluate investment risk in highway infrastructure projects.
Furthermore, research on district cooling systems shows that Value at Risk (VaR),
Conditional Value at Risk (CVaR), and stochastic modeling methods
can help organizations more realistically assess investment uncertainty in the
energy sector. In the shipping investment sector, dynamic modeling and scenario
analysis also help organizations evaluate investment risk more accurately and
quantifiably.
Attention to sustainability and green investment has also
led to the development of financial risk assessment in the renewable energy and
sustainable finance sectors. Research on biofuel supply chains under
uncertainty shows that uncertainty modeling is a crucial component of
sustainable investment risk management. Furthermore, research on renewable
hydrogen production explains that techno-economic analysis and financial
risk assessment are used to evaluate the feasibility of environmentally
friendly energy investments and to support the development of zero-carbon
energy systems. Other research on energy-efficient building renovation also
demonstrates that technical-financial risk assessment plays a crucial
role in the development of green investment and sustainable infrastructure
management.
As digital technology advances, financial risk
assessments are increasingly using predictive analytics and data-driven
decision-making. Research on the internet financial industry shows that machine
learning and deep learning can improve the accuracy of financial predictions
and credit risk assessments for companies. Furthermore, the use of deep
neural networks in the foreign exchange market helps organizations and
investors evaluate investment risks more effectively. Other research also shows
that anomaly detection, graph neural networks, and nonlinear
causal time-series networks can improve the accuracy of financial risk
predictions in complex, imbalanced data environments.
Gaps remain
that require attention. First, financial risk assessment research remains
fragmented across various industrial sectors, resulting in limited research that
comprehensively addresses method development from a management perspective.
Second, most research focuses on developing technical methods rather than
integrating financial risk assessment with strategic organizational
decision-making. Third, research integrating enterprise risk management,
sustainability finance, quantitative modeling, and organizational management
remains relatively limited.
Based on these conditions, this study aims to analyze the
development of methods, implementation, and research trends in Financial
Risk Assessment Management in various industrial sectors through a Systematic
Literature Review (SLR) approach. The study used the PRISMA 2020 method,
with data sources derived from the Scopus database using the keywords "financial
risk assessment" and "enterprise risk management". A
total of 25 international articles relevant to the field of management were
selected through a screening and quality evaluation process to provide a
comprehensive overview of the development of financial risk assessment in
modern organizations.
Formulation
of the problem
In the Systematic Literature Review (SLR) on Financial Risk Assessment Management, it is
important to understand how research on financial risk assessment has developed
over the past 10 years in the Scopus database (RM1). Analysis of these
publications can provide an overview of research trends, methodological
developments, and the direction of financial risk assessment studies in modern
management. Furthermore, this study aims to examine the relationship between
topics and keywords that most frequently appear in financial risk assessment
research (RM2), thereby identifying the main themes that are the focus of
research across various industrial sectors.
This study will also analyze the distribution of articles
based on the financial risk assessment methods and approaches used (RM3), such
as Analytic Hierarchy Process (AHP), fuzzy logic, Monte Carlo
Simulation, Value at Risk (VaR), Conditional Value at Risk (CVaR),
predictive analytics, and various other quantitative modeling
approaches. In addition, this study will identify the sectors or research areas
most discussed in financial risk assessment studies (RM4), including
healthcare, tourism, supply chain finance, internet finance, renewable energy,
infrastructure projects, and sustainability finance.
Furthermore, this study will also examine how financial
risk assessment is used to support organizational decision-making and risk
management in modern enterprises (RM5). This study will examine how financial
risk assessment helps organizations improve the effectiveness of risk control,
maintain financial stability, and support business sustainability across
various industrial sectors. Furthermore, this study will identify key factors
influencing the development of Financial Risk Assessment Management in
modern organizations (RM6), including digital transformation, financial
technology developments, sustainability finance, quantitative modeling, and
enterprise risk management.
Thus, this study aims to provide a more comprehensive
understanding of the development, implementation, and direction of Financial
Risk Assessment Management research from a management perspective over the
past 10 years.
METHOD
In this study, the primary data sources used were
international scientific articles published in the Scopus database within the
last 10 years, from 2016 to 2025. These articles were selected for their
relevance to Financial Risk Assessment Management from a management
perspective. The literature used includes research on enterprise risk
management, financial risk assessment, sustainability finance, supply chain
finance, internet finance, quantitative modeling, and various modern financial
risk management approaches (Li et al., 2018) (Brunner-kirchmair, 2019) (Yan & Wang, 2020). All articles were from international journals indexed
by Scopus and selected based on research quality, topic relevance, and
theoretical and practical contributions to the development of financial risk
management (İşletmelerinde &
Risklerin, 2021) (Wang, 2025).
The method used in this study is a Systematic
Literature Review (SLR) following the PRISMA 2020 approach (Preferred
Reporting Items for Systematic Reviews and Meta-Analyses). The SLR method
is used to systematically identify, evaluate, and synthesize research to
provide a comprehensive overview of the development of Financial Risk
Assessment Management research (Tranfield et
al., 2003). This approach was chosen because it can help
researchers understand the development of methods, the implementation of
financial risk assessment, and research trends in various industrial sectors (Kitchenham, 2004).
The article search was conducted in the Scopus database
using the keywords "financial risk assessment" and "enterprise
risk management." The identification stage yielded 79 articles related
to the research topic. Next, a screening process was conducted based on
the suitability of the title, abstract, year of publication, and relevance to
the management field. Articles that were irrelevant, duplicate, or not in line
with the research focus were eliminated at this stage (Moher et al., 2009) (Liberati et al., 2009).
The next stage is eligibility, which is the
process of evaluating the suitability of articles based on research content,
methods used, research contributions, and their relevance to Financial Risk
Assessment Management. After the evaluation process, 25 international
articles met the inclusion criteria and were used as primary sources in this
study. This approach was taken to ensure that the articles used were of good
research quality and relevant to a management perspective (Page et al., 2021).
Data from the selected articles were then analyzed using
a descriptive approach and literature synthesis. The analysis was conducted to
identify developments in financial risk assessment methods, the most researched
industrial sectors, risk management approaches used, and research trends over
the past 10 years (Kumar et al., 2017) (Franzoni et al., 2019) (Zhao, 2024). Furthermore, this study analyzes the development of
financial risk assessment to support organizational decision-making, corporate
risk control, sustainability finance, and enterprise risk management in modern
organizations (Martínez-ruiz et al.,
2023) (Wang, 2025) (Su & Zhou, 2018).
Systematic
Literature Search
The development of globalization, digital transformation,
and increasing economic uncertainty have caused organizations to face
increasingly complex financial risks, making Financial Risk Assessment
Management a crucial part of modern management systems (Li et al., 2018) (Brunner-kirchmair, 2019) (Yan & Wang, 2020). Financial risk assessment is not only used to identify
and control a company's financial risks but also supports strategic
decision-making, maintains organizational stability, and improves business
sustainability across various industrial sectors (İşletmelerinde &
Risklerin, 2021)(Franzoni et al., 2019) (ZHYKHARIEVA1 et al.,
2025). This study aims to systematically review the
development of research on Financial Risk Assessment Management from a
management perspective over the past 10 years.
This study explores various financial risk assessment
methods such as Analytic Hierarchy Process (AHP), fuzzy logic, Monte
Carlo Simulation, Value at Risk (VaR), Conditional Value at Risk (CVaR),
predictive analytics, and quantitative modeling used in various industrial
sectors (Kumar et al., 2017) (Yang & Estimation,
2024) (Su & Zhou, 2018). Furthermore, this study also identifies the
implementation of financial risk assessment in the healthcare, tourism, supply
chain finance, internet finance, renewable energy, and sustainability finance
sectors (Su & Zhou, 2018) (İşletmelerinde &
Risklerin, 2021)(Wang, 2025). Using a Systematic Literature Review (SLR)
approach, this study collects and analyzes prior studies to provide a more
comprehensive understanding of the development of methods, implementation, and
research directions in Financial Risk Assessment Management.
The Scopus database was used because it has a broad,
structured, and reliable coverage of international scientific literature for
SLR-based research. The article search was conducted in Scopus using the
keywords "financial risk assessment" and "enterprise
risk management" in the title, abstract, and keywords
sections. To facilitate the identification and screening of articles,
researchers used the Web AI platform Watase as an AI-assisted literature
search tool to support the search, management, and organization of research
data more systematically. However, all primary data sources in this study still
come from the Scopus database.
The data collection stages were carried out by: (1)
opening the Scopus database via Scopus.com; (2) entering search keywords; (3)
limiting the type of document to journal articles only ; (4) determining
the range of publication years for the last 10 years, starting from 2016 to
2025. This year was chosen based on the number of studies that began to
research the topic, and 2026 was not selected because the year was still
ongoing; and (5) conducting the article search process in the Scopus database.
Based on the initial search results, 79 articles were obtained related to the
research topic.
The obtained data were then stored in CSV and RIS formats
to facilitate reference management and data analysis. A screening process
was then conducted using inclusion and exclusion criteria based on the
suitability of the title and abstract, the year of publication, the research's
relevance to the management field, and the article's relationship to the topic
of Financial Risk Assessment Management. Articles that were irrelevant,
duplicate, or not in line with the research focus were excluded, leaving 25
international articles that met the inclusion criteria and served as the
primary sources for the research.
The research used the PRISMA 2020 (Preferred Reporting
Items for Systematic Reviews and Meta-Analyses) approach, which comprises
the identification, screening, eligibility, and inclusion stages (Moher et al., 2009) (Liberati et al., 2009). This approach was used to ensure that the article
selection process was carried out systematically, transparently, and in a
structured manner, thereby improving the validity and quality of the research
results (Tranfield et al., 2003) (Kitchenham, 2004).
RESULTS
AND DISCUSSION
Trends
in Financial Risk Assessment Management Research Publications in the Last 10
Years
To gain a broader overview of the development of research
in Financial Risk Assessment Management, this study analyzes publication
trends over the past 10 years using the Scopus database. Publication trend
analysis is important for observing research developments, changes in focus,
and the growing attention to financial risk assessment from a management
perspective.
Based on data collection results, research on Financial
Risk Assessment Management has shown significant progress in recent years.
In the early stages of research, publications on financial risk assessment were
relatively few, with most focusing on traditional approaches such as financial
ratio analysis, the Analytic Hierarchy Process (AHP), and fuzzy
logic. However, with the development of digital transformation,
sustainability finance, and quantitative modeling, the number of studies on
financial risk assessment has been increasing.
The increase in publications became evident after 2018,
when various studies began discussing financial risk assessment across internet
finance, supply chain finance, renewable energy, and sustainability management.
Furthermore, the development of digital technology and the increasing use of
predictive analytics have driven research on machine learning, deep learning,
anomaly detection, and dynamic risk assessment in organizational financial risk
management.
Based on the analysis, the highest increase in
publications occurred in the period following the COVID-19 pandemic. This
situation indicates that global economic uncertainty, changes in the business
environment, and rising financial risks have heightened the need for more
adaptive, rapid, and integrated financial risk assessment systems for
organizations. Furthermore, recent research indicates a shift in focus from
traditional financial risk assessment approaches to predictive modeling,
sustainability finance, and enterprise risk management. This increase can be
seen in the following figure:
Figure 1. Publication trend graph
Relationship
Between Keywords (Research Topics)
Figure 2. Word Cloud (Keyword Frequency Map)
A keyword relationship analysis was conducted to identify
the most frequently occurring research themes in Financial Risk Assessment
Management studies. This visualization illustrates the interrelationships
among research topics, keyword frequency, and the direction of research
development, based on articles that have passed the screening process.
In this visualization, the font size indicates the
frequency of a keyword's appearance in the analyzed literature. The larger the
font size of a keyword, the more frequently it appears in research. Conversely,
keywords with smaller font sizes indicate that the topic is relatively limited
or under-researched. Furthermore, keywords positioned in the center of the
visualization indicate a stronger connection to other research topics, while
keywords located at the edges generally have a more specific relationship to a
particular theme.
Based on the visualization results in Figure 2, the
keyword "financial risk assessment" is the most dominant topic
because it has the largest font size and is located in the center of the
visualization. This finding indicates that financial risk assessment is the
primary focus of the analyzed research. Furthermore, several other keywords
with high frequency of occurrence are "risk assessment,"
"financial risk," "risk management," "supply chain
risk management," and "supply chain finance." These keywords form
the core of research on the evaluation and management of financial risk across
various organizational and industrial sectors.
The visualization also shows a strong correlation between
financial risk assessment and various analytical methods, such as the analytic
hierarchy process, Monte Carlo simulation, machine learning, deep learning,
artificial intelligence, graph neural networks, feature selection, and credit
risk assessment. This demonstrates that financial risk assessment research
continues to evolve, utilizing quantitative approaches and more complex
analytical techniques to improve the accuracy of financial risk measurement.
In addition to the methodological aspects, several
keywords related to the research object also appear quite dominant, such as
internet finance, listed tourism companies, renewable energy, biofuels,
shipping companies, marine engineering, healthcare, and cloud accounting. These
findings indicate that the application of financial risk assessment is not
limited to the traditional financial sector but has expanded to various
industries with high levels of uncertainty and financial risk.
Overall, the visualization results show that research in Financial
Risk Assessment Management has experienced quite rapid development in
recent years. The focus of research is not only on financial risk measurement
but also on the development of analytical methods, managerial decision-making,
supply chain risk management, business sustainability, and risk evaluation in
increasingly complex industrial sectors. Thus, financial risk assessment has
become a central theme in supporting the effectiveness of risk management and
strategic decision-making in modern organizations.
Distribution article based on Method
Study
Metadata processing (CSV) also helps
provide answers about the method used in each article. Type method which is
intended to be presented in the following image:
Figure 3. Research
Methods for Each Article
Based on Figure 3, the most frequently used research
method was quantitative, with 13 articles (50%). Furthermore, experimental
model methods were used in 10 articles (42.3%). Meanwhile, conceptual,
qualitative, and mixed methods were each used in only one article (3.8%). These
findings indicate that research in Financial Risk Assessment Management is
dominated by quantitative approaches and model development, reflecting the high
demand for data-driven financial risk measurement, prediction, and evaluation to
support modern organizational decision-making.
Distribution article based on Object
Study
In addition to the distribution of research methods, the
analysis of article data also provides information on the research objects used
in each study. Generally, research objects in the study of Financial Risk
Assessment Management are grouped into two categories: business and non-business.
The business category includes companies and various industrial sectors such as
finance, healthcare, tourism, energy, logistics, supply chains, and state-owned
enterprises. Meanwhile, the non-business category includes research focused on
developing models, algorithms, conceptual frameworks, and methods for financial
risk analysis without a specific industry objective. This division helps
explain research trends in the application and development of Financial Risk
Assessment Management methods.
Figure 4. Object Research For Each Article
In addition to the distribution of research methods, the
analysis of the 25 selected articles also provides information on the research
objects used in the Financial Risk Assessment Management study. Based on
Figure 4, the research objects are dominated by business or industrial
fields, accounting for 84% (21 articles). Meanwhile, research
objects that fall into the non-business or conceptual category are only 16%,
or 4 articles.
The predominance of research subjects in the business
sector indicates that Financial Risk Assessment Management research is
more focused on solving real-world problems faced by organizations and
companies. The various industrial sectors covered include supply chain
finance, internet finance, healthcare organizations, tourism
companies, the hospitality industry, marine enterprises, port
enterprises, renewable energy, biofuel supply chains, and state-owned
enterprises (SOEs). These findings demonstrate that financial risk
management has become a critical requirement for supporting business
continuity, improving decision-making quality, and minimizing potential
organizational losses.
On the other hand, research with non-business or
conceptual objectives is still relatively limited. This research group
generally focuses on developing models, algorithms, conceptual frameworks, and
analytical methods to improve the accuracy and effectiveness of the financial
risk assessment process, without being specifically applied to any industry
sector. Although fewer in number, research in this category has made
significant contributions to the development of theories, methods, and
innovations that underpin the application of financial risk assessment across
various business sectors.
Overall, these results indicate that research in Financial
Risk Assessment Management over the past 10 years has focused more on
practical business applications than on conceptual development. This indicates
that organizations' need for effective financial risk management systems
continues to grow in line with the growing complexity of the business
environment and global economic uncertainty.
Table 1. Classification method And object
study
No
Article Code
Year
Researchers
Method
Object
Quantitative
Qualitative
Mixture
Business
Non-Business
1
M1
2018
(Li
et al., 2018)
1
1
2
M2
2017
(Kumar
et al., 2017)
1
1
3
M3
2019
(Brunner-kirchmair,
2019)B
1
1
4
M4
2019
(Franzoni
et al., 2019)
1
1
5
M5
2018
(Su
& Zhou, 2018)
1
1
6
M6
2020
(Yan
& Wang, 2020)
1
1
7
M7
2025
(Liu
et al., 2025)
1
1
8
M8
2023
(Fan
et al., 2023)
1
1
9
M9
2024
(Yang
& Estimation, 2024)
1
1
10
M10
2021
(İşletmelerinde
& Risklerin, 2021)
1
1
11
M11
2022
(Elston,
2022)
1
1
12
M12
2022
(Chi
et al., 2022)
1
1
13
M13
2022
(Lin
et al., 2022)
1
1
14
M14
2022
(Andaloro
et al., 2022)
1
1
15
M15
2023
(Zou,
2024).
1
1
16
M16
2023
(Hirsa
& Lin, 2020)
1
1
17
M17
2016
(Santibañez-aguilar
et al., 2016)
1
1
18
M18
2016
(Santibañez-aguilar
et al., 2016)
1
1
19
M19
2025
(Wang,
2025)
1
1
20
M20
2025
(Kondoju
& Bindewari, 2025)
1
1
21
M21
2025
(Naveed
et al., 2025)
1
1
22
M22
2025
(Liu
et al., 2025)
1
1
23
M23
2025
(ZHYKHARIEVA1
et al., 2025)
1
1
24
M24
2022
(Bingler
et al., 2022)
1
1
25
M25
2023
(Lu
et al., 2023)
1
1
Table 2. Classification Researchers, Title, Variables And Results Study
NO
Year
Researchers
Title
Variables
Research result
1
2018
(Li et al., 2018)
A Novel
Financial Risk Assessment Model for Companies Based on Heterogeneous
Information and Aggregated Historical Data
Independent:
Heterogeneous Information;
Dependent:
Financial Risk Assessment; Supporting: Historical Aggregated Data
The study
developed a financial risk assessment model based on heterogeneous
information and historical company data. The results showed that integrating
multiple information sources improved the accuracy of risk identification
compared to conventional approaches and helped detect potential financial
distress earlier.
2
2018
(Kumar et al., 2017)
Financial Risk
Assessment and Modeling of PPP Based Indian Highway Infrastructure Projects
Independent:
Project Risk; Dependent: Financial Risk Assessment; Moderation: Monte Carlo
Simulation
Monte Carlo
simulation has proven effective in evaluating uncertainty in infrastructure
project investments. The developed model can more accurately estimate risk
levels and help investors mitigate potential investment losses.
3
2019
(Brunner-kirchmair, 2019)
Knowledge is
Power – Conceptualizing Collaborative Financial Risk Assessment
Independent:
Collaborative Knowledge;
Dependent:
Financial Risk Assessment
Collaboration
and knowledge exchange between stakeholders improves the quality of the
financial risk assessment process. A collaborative approach results in more
effective and comprehensive risk management decisions.
4
2019
(Franzoni et al., 2019)
Rainfall
Financial Risk Assessment in the Hospitality Industry
Independent:
Rainfall Intensity;
Dependent:
Hospitality Financial Risk
Weather factors
such as rainfall impact the financial health of the hospitality industry.
External risks have been shown to impact customer visitation rates and
company revenue.
5
2018
(Su & Zhou, 2018)
Financial Risk
Assessment of Listed Tourism Companies Based on Gray Relational Degree Model
Independent:
Financial Indicators;
Dependent:
Financial Risk Assessment
The Grey
Relational Degree Model can effectively identify the risk level of tourism
companies. This model can be used as a basis for management and investment
decisions.
6
2020
(Yan & Wang, 2020)
Research on
Risk Assessment and Control of Internet Finance in China Based on FAHP
Independent:
Internet Finance Risk;
Dependent: Risk
Control; Supporter: FAHP
The FAHP method
can improve the effectiveness of risk evaluation and control in the internet
finance sector. The model helps organizations determine more appropriate risk
mitigation strategies.
7
2025
(Liu et al., 2025)
Financial Risk
Assessment and Pre-warning of Port Enterprises Based on Neural Network Model
Independent:
Neural Network Model;
Dependent:
Financial Risk Assessment
A neural
network can detect potential financial risks in port companies earlier. The
developed early warning system helps organizations reduce the likelihood of
financial distress.
8
2023
(Fan et al., 2023)
Financial Risk
Assessment of Marine Enterprises Based on Analytic Hierarchy Process
Independent:
AHP; Dependent: Financial Risk Assessment
AHP helps
maritime companies systematically evaluate and prioritize financial risks,
thereby increasing the effectiveness of risk management decision-making.
9
2024
(Yang & Estimation, 2024)
Research on
Supply Chain Financial Risk Assessment Based on Blockchain and Fuzzy Neural
Networks
Independent:
Blockchain Technology;
Dependent:
Supply Chain Financial Risk
Blockchain
increases data transparency in supply chain finance, while fuzzy neural
networks enhance the system's ability to identify and predict financial
risks.
10
2021
(İşletmelerinde & Risklerin, 2021)
Financial Risk
Assessment in Healthcare Organizations
Independent:
Procurement Risk;
Dependent:
Healthcare Financial Risk
Financial risk
assessment helps healthcare organizations control procurement, liquidity, and
resource utilization risks, thereby improving the organization's operational
stability.
11
2022
(Elston, 2022)
Financial Risk
Assessment to Improve the Accuracy of Financial Prediction in the Internet
Financial Industry Using Data Analytics Models
Independent:
Data Analytics; Dependent: Financial Prediction Accuracy
Data analytics,
machine learning, and deep learning are improving the accuracy of financial
risk predictions in the internet finance industry. Models are able to
identify risk patterns that are difficult to detect with traditional methods.
12
2022
(Chi et al., 2022)
Financial Risk
Assessment of Photovoltaic Industry Listed Companies Based on Text Mining
Independent:
Text Mining; Dependent: Financial Risk Assessment
Text mining is
able to extract important information from company documents and support
early warning systems for financial risks more quickly and accurately.
13
2022
(Lin et al., 2022)
Financial Risk
Assessment of Enterprise Management Accounting Based on Association Rule
Algorithm under the Background of Big Data
Independent:
Big Data; Dependent: Financial Risk Assessment
Association
Rule Algorithm in big data environment increases the effectiveness of
financial risk evaluation and helps to understand the relationship between
company financial indicators.
14
2022
(Andaloro et al., 2022)
De-Risking the
Energy Efficient Renovation of Commercial Office Buildings through
Technical-Financial Risk Assessment
Independent:
Technical Risk; Dependent: Financial Risk Assessment
Integration of
technical and financial aspects can reduce investment uncertainty in
energy-efficient building projects and improve the quality of investment
decision-making.
15
2024
(Zou, 2024)
Financial Risk
Assessment Management of State-Owned Enterprises Based on Cloud Accounting in
the Era of Big Data
Independent:
Big Data; Dependent: Financial Risk Management
Cloud
accounting and big data increase the effectiveness of financial risk
management and provide faster and more accurate information for management.
16
2020
(Hirsa & Lin, 2020)
Explainable AI
in Credit Risk Management
Independent:
Explainable AI; Dependent: Credit Risk Management
Explainable AI
improves the transparency and interpretability of credit risk assessment
models, thereby increasing user confidence in the prediction results.
17
2016
(Santibañez-aguilar et al., 2016)
Financial Risk
Assessment and Optimal Planning of Biofuels Supply Chains under Uncertainty
Independent:
Supply Chain Uncertainty;
Dependent:
Financial Risk Assessment
Biofuel supply
chain uncertainty significantly impacts financial risk. The developed model
helps organizations determine optimal operational strategies.
18
2024
(Zhao, 2024)
Financial Risk
Assessment Management of Cloud Accounting SOEs Based on Spillover Index
Independent:
Spillover Index;
Dependent:
Financial Risk Management
The spillover
index helps identify the relationship and distribution of risks between
sectors, thereby increasing the effectiveness of a company's risk mitigation
strategy.
19
2025
(Wang, 2025)
Credit Risk
Assessment of Green Supply Chain Finance for SMEs Based on Multi-Source
Information Fusion
Independent:
Multi-Source Information Fusion; Dependent: Credit Risk Assessment
Integration of
various information sources improves the accuracy of credit risk assessments
in MSMEs and supports more appropriate financing decision-making.
20
2025
(Kondoju & Bindewari, 2025)
Leveraging AI
for Dynamic Risk Assessment in Financial Services
Independent:
Dynamic Risk Assessment;
Dependent:
Financial Service Risk
Dynamic risk
assessment enables organizations to conduct real-time risk evaluations,
thereby improving their ability to respond to changing market conditions.
21
2025
(Naveed et al., 2025)
Financial
Modeling System Using Deep Neural Networks (DNNs) for Financial Risk
Assessments
Independent:
Deep Neural Network;
Dependent:
Financial Risk Assessment
Deep Neural
Networks produce higher risk prediction accuracy than conventional methods
and are capable of processing complex financial data.
22
2025
(Liu et al., 2025)
Supply Chain
Financial Risk Assessment: A Modified Graph Attention Neural Network
Independent:
Graph Attention Neural Network; Dependent: Supply Chain Financial Risk
Graph Attention
Neural Network improves the system's ability to identify and predict supply
chain financial risks with a higher degree of accuracy.
23
2025
(ZHYKHARIEVA1
et al., 2025)
Financial Risk
Assessment Based on a Dynamic Model: A Case of Shipping Investments
Independent
Variables: Freight Rates, Operating Expenditure (OPEX), Financing Conditions,
Debt Structure, Investment Cost. Dependent Variable: Financial Risk
Assessment. Supporting Variables: Dynamic Financial Model, Scenario Analysis,
DCF, NPV, IRR.
Independent
Variables: Freight Rates, Operating Expenditure (OPEX), Financing Conditions,
Debt Structure, Investment Cost. Dependent Variable: Financial Risk
Assessment. Supporting Variables: Dynamic Financial Model, Scenario Analysis,
DCF, NPV, IRR. This study developed a dynamic financial model
(three-statement financial model) that integrates the income statement,
balance sheet, and cash flow to assess the financial risk of shipping
investments. The results show that freight rate volatility, operating costs,
and the financing structure are the main factors influencing investment risk.
The scenario analysis yielded 27 possible conditions with varying DCF values.
The project risk is classified as high with a coefficient of variation of
56%, while the probability of a negative DCF is 2.3%. This model is effective
for supporting investment decisions, financing, and risk mitigation
strategies in shipping companies.
24
2022
(Bingler
et al., 2022)
Taming the
Green Swan: A Criteria-Based Analysis to Improve the Understanding of
Climate-Related Financial Risk Assessment Tools
Independent
Variables:
Accountability, Depth of Risk Analysis, and Usability. Dependent Variable:
Quality of Climate-Related Financial Risk Assessment Tools. Indicators:
Model transparency, emission data strategy, scientific foundation, hazard
coverage, exposure, vulnerability and resilience, adaptability, economic
impact, risk amplification, output interpretability, and uncertainty
management.
The study
developed a criteria-based framework to evaluate the quality of
climate-related financial risk assessment tools. An assessment of 16 climate
transition risk tools revealed that most tools demonstrated moderate
performance, while only a few achieved high performance across all evaluation
criteria. The findings indicate that model transparency, scenario
flexibility, assumption communication, and uncertainty management remain the
main weaknesses of existing tools. Significant variation was also observed in
the depth of risk analysis performed by different tools. The study recommends
improving methodological transparency, adopting standardized reporting
templates, and presenting risk outcomes through probability distributions or
confidence intervals to enhance comparability, interpretability, and decision
usefulness for regulators, investors, and other stakeholders
25
2023
(Lu
et al., 2023)
Profit vs.
Equality? The Case of Financial Risk Assessment and A New Perspective on
Alternative Data
Independent
Variables: Alternative Data. Dependent Variables: Credit Risk Assessment,
Financial Inclusion, and Profitability
Alternative
data, particularly smartphone activity data, improve credit risk assessment
accuracy, increase financial inclusion, and enhance lending profitability
while reducing potential bias in credit decisions.
Manajemen Penilaian Risiko Keuangan pada Organisasi
Modern: Suatu Tinjauan Literatur Sistematis tentang Metode dan Pendekatan
Manajemen Risiko
Financial Risk Assessment Management in Modern
Organizations: A Systematic Literature Review of Risk Management Methods and
Approaches
Desi Rosalina 1,2 , Nera Marinda Machdar 3
, Adler Haymans Manurung 4
Bhayangkara University, Greater Jakarta, Indonesia 1,3,4
Adzkia University, Padang, Indonesia 2
202530151001@mhs.ubharajaya.ac.id
desirosalina@adzkia.ac.id
Abstrak
Globalisasi,
transformasi digital, dan meningkatnya ketidakpastian ekonomi menyebabkan
organisasi menghadapi risiko keuangan yang semakin kompleks. Kondisi tersebut
menegaskan pentingnya penerapan Manajemen Penilaian Risiko Keuangan sebagai
bagian dari pengambilan keputusan strategis dan manajemen risiko organisasi.
Penelitian ini bertujuan menganalisis perkembangan metode, implementasi, tren
penelitian, serta faktor-faktor yang memengaruhi Manajemen Penilaian Risiko
Keuangan pada organisasi modern. Penelitian menggunakan metode Systematic
Literature Review (SLR) dengan pendekatan PRISMA 2020. Data diperoleh dari
basis data Scopus menggunakan kata kunci financial risk assessment dan enterprise
risk management. Proses identifikasi menghasilkan 79 artikel, kemudian
melalui tahap penyaringan dan evaluasi hingga diperoleh 25 artikel
internasional yang memenuhi kriteria penelitian.
Hasil
penelitian menunjukkan bahwa publikasi mengenai Financial Risk Assessment
Management meningkat secara signifikan dalam sepuluh tahun terakhir, khususnya
setelah tahun 2018. Penelitian didominasi oleh metode kuantitatif (50%) dan
eksperimen berbasis model (42,3%), dengan objek penelitian yang sebagian besar
berasal dari sektor bisnis dan industri (84%). Selain itu, penelitian
menunjukkan adanya pergeseran dari metode tradisional seperti Analytic
Hierarchy Process (AHP), Fuzzy Logic, dan Monte Carlo Simulation menuju
pendekatan berbasis teknologi seperti machine learning, deep learning,
artificial intelligence, text mining, graph neural networks, dan predictive
analytics. Implementasi penilaian risiko keuangan juga telah berkembang pada
berbagai sektor seperti kesehatan, pariwisata, rantai pasok, energi terbarukan,
keuangan digital, dan perusahaan milik negara.
Penelitian
ini menyimpulkan bahwa kualitas data, metode analisis, teknologi digital,
karakteristik industri, ketidakpastian lingkungan bisnis, serta aspek
keberlanjutan merupakan faktor utama yang memengaruhi efektivitas Manajemen
Penilaian Risiko Keuangan. Oleh karena itu, organisasi perlu mengembangkan
sistem penilaian risiko yang lebih terintegrasi, adaptif, dan berbasis data
guna meningkatkan kualitas pengambilan keputusan dan mendukung keberlanjutan
bisnis di masa depan.
Kata
Kunci: Manajemen Penilaian
Risiko Keuangan, Penilaian Risiko Keuangan, Manajemen Risiko Perusahaan,
Manajemen Risiko, Tinjauan Literatur Sistematis.
Abstract
Globalization,
digital transformation, and increasing economic uncertainty have caused
organizations to face increasingly complex financial risks. These conditions
emphasize the importance of implementing Financial Risk Assessment Management
as part of strategic decision-making and organizational risk management. This
study aims to analyze the development of methods, implementation, research
trends, and factors influencing Financial Risk Assessment Management in modern
organizations. The method used was a Systematic Literature Review (SLR) with
the PRISMA 2020 approach. Data sources were sourced from the Scopus database
using the keywords "financial risk assessment" and "enterprise
risk management." The identification process yielded 79 articles, which
were then screened, assessed for eligibility, and included, resulting in 25
international articles meeting the research criteria.
The
results show that publications on Financial Risk Assessment Management have
increased significantly over the past ten years, particularly after 2018.
Research is dominated by quantitative methods (50%) and model experiments
(42.3%), while the research subjects largely focused on the business and
industrial sectors (84%). The findings also indicate a shift from traditional
methods, such as the Analytic Hierarchy Process (AHP), Fuzzy Logic, and Monte
Carlo Simulation, to technology-based approaches, including machine learning,
deep learning, artificial intelligence, text mining, graph neural networks, and
predictive analytics. Furthermore, the implementation of financial risk
assessment has expanded across various sectors, including healthcare, tourism, supply
chains, renewable energy, digital finance, and state-owned enterprises.
This
study concludes that data quality, analysis methods, digital technology,
industry characteristics, business environment uncertainty, and sustainability
aspects are key factors influencing the effectiveness of Financial Risk
Assessment Management. Therefore, organizations need to develop more
integrated, adaptive, and data-driven risk assessment systems to improve
decision-making quality and support future business sustainability.
Keywords: Financial Risk Assessment, Financial Risk Assessment
Management, Enterprise Risk Management, Risk Management, Systematic Literature
Review.
INTRODUCTION
Globalization, digital transformation, and global
economic uncertainty are creating increasingly complex and dynamic financial
risks for organizations. Changing market conditions, developments in financial
technology, increased business competition, and economic instability are
driving companies to strengthen their Financial Risk Assessment Management
systems as a crucial component of strategic organizational decision-making (Li et al., 2018) (Franzoni et al., 2019) (Zhao, 2024). Financial risks not only impact a company's financial
stability but also impact business sustainability, operational effectiveness,
organizational reputation, and even investor confidence (İşletmelerinde &
Risklerin, 2021) (Fan et al., 2023).
Financial risk assessment is the process of identifying,
measuring, analyzing, and controlling financial risks to help organizations
minimize potential losses and improve the quality of managerial
decision-making. Over time, financial risk assessment has transformed from a
traditional approach to a more systematic, integrated, and quantitative
modeling-based approach (Kumar et al., 2017)(Li et al., 2018) explain that
using heterogeneous information and historical aggregated data can
improve the quality of a company's risk predictions. Furthermore, research by (Brunner-kirchmair, 2019) confirms that collaborative financial risk assessment is
a crucial part of modern organizations because risk management requires the
involvement of various stakeholders in the decision-making process.
In practice, financial risk assessment has been applied
across various industrial sectors. In the healthcare sector,(Kondoju & Bindewari,
2025) explain that healthcare organizations require an enterprise
risk management system to control procurement risk, liquidity
risk, and operational financial risk to maintain organizational
stability. In the tourism sector, (Su & Zhou, 2018) show that tourism companies face significant financial
risks due to market changes and industry uncertainty, thereby requiring a risk
evaluation system that supports business sustainability. Furthermore, Franzoni
and Pelizzari (2019) explain that external factors, such as changes in weather
and rainfall, can also affect the profitability of hospitality companies,
requiring a scenario-based financial risk assessment system to mitigate
the impact of these risks on business performance.
The development of financial risk assessment is also
evident in the maritime and logistics sectors. Research on the Financial
Risk Assessment of Marine Enterprises Based on the Analytic Hierarchy Process shows
that the Analytic Hierarchy Process (AHP) method can help companies
evaluate risks through systematic risk indicator weighting. Furthermore,
research on the Financial Risk Assessment and Pre-warning of Port
Enterprises Based on a Neural Network Model indicates that pre-warning and
early warning systems can help port companies detect potential financial
distress more quickly, thereby enabling more effective risk management
decisions.
Digital transformation and the development of internet
finance have increased the complexity of financial risks in modern
organizations. (Yan & Wang, 2020) explain that
internet finance carries high risks in operational risk, credit risk,
legal risk, and internet security risk, thus requiring a more
adaptive risk management approach. Research by (Wang, 2025) also shows that blockchain and fuzzy neural networks can
be used to increase transparency and effectiveness in risk management in supply
chain finance. Furthermore,(Zhao, 2024) explains that the use of spillover indexes and VAR
models can help organizations understand the relationships among financial
markets and control spillover risks in modern corporate financial systems.
Besides its application in the organizational and
financial services sectors, financial risk assessment is also developing in the
fields of supply chain finance and green finance. Research by (Wang, 2025) shows that integrating multiple information sources can
improve the quality of credit risk evaluations in green supply chain
finance for small and medium-sized enterprises. Other research explains the
use of graph attention neural networks to improve the effectiveness of
risk evaluation in supply chain finance by adopting a more complex, dynamic
relational modeling approach. This demonstrates that modern financial risk
management is increasingly moving towards integrated, data-driven systems.
The development of financial risk assessment methods also
demonstrates the growing use of quantitative modeling in organizational
decision-making. (Kumar et al., 2017) explain that Monte Carlo Simulation and NPV-at-Risk
are used to evaluate investment risk in highway infrastructure projects.
Furthermore, research on district cooling systems shows that Value at Risk (VaR),
Conditional Value at Risk (CVaR), and stochastic modeling methods
can help organizations more realistically assess investment uncertainty in the
energy sector. In the shipping investment sector, dynamic modeling and scenario
analysis also help organizations evaluate investment risk more accurately and
quantifiably.
Attention to sustainability and green investment has also
led to the development of financial risk assessment in the renewable energy and
sustainable finance sectors. Research on biofuel supply chains under
uncertainty shows that uncertainty modeling is a crucial component of
sustainable investment risk management. Furthermore, research on renewable
hydrogen production explains that techno-economic analysis and financial
risk assessment are used to evaluate the feasibility of environmentally
friendly energy investments and to support the development of zero-carbon
energy systems. Other research on energy-efficient building renovation also
demonstrates that technical-financial risk assessment plays a crucial
role in the development of green investment and sustainable infrastructure
management.
As digital technology advances, financial risk
assessments are increasingly using predictive analytics and data-driven
decision-making. Research on the internet financial industry shows that machine
learning and deep learning can improve the accuracy of financial predictions
and credit risk assessments for companies. Furthermore, the use of deep
neural networks in the foreign exchange market helps organizations and
investors evaluate investment risks more effectively. Other research also shows
that anomaly detection, graph neural networks, and nonlinear
causal time-series networks can improve the accuracy of financial risk
predictions in complex, imbalanced data environments.
Gaps remain
that require attention. First, financial risk assessment research remains
fragmented across various industrial sectors, resulting in limited research that
comprehensively addresses method development from a management perspective.
Second, most research focuses on developing technical methods rather than
integrating financial risk assessment with strategic organizational
decision-making. Third, research integrating enterprise risk management,
sustainability finance, quantitative modeling, and organizational management
remains relatively limited.
Based on these conditions, this study aims to analyze the
development of methods, implementation, and research trends in Financial
Risk Assessment Management in various industrial sectors through a Systematic
Literature Review (SLR) approach. The study used the PRISMA 2020 method,
with data sources derived from the Scopus database using the keywords "financial
risk assessment" and "enterprise risk management". A
total of 25 international articles relevant to the field of management were
selected through a screening and quality evaluation process to provide a
comprehensive overview of the development of financial risk assessment in
modern organizations.
Formulation
of the problem
In the Systematic Literature Review (SLR) on Financial Risk Assessment Management, it is
important to understand how research on financial risk assessment has developed
over the past 10 years in the Scopus database (RM1). Analysis of these
publications can provide an overview of research trends, methodological
developments, and the direction of financial risk assessment studies in modern
management. Furthermore, this study aims to examine the relationship between
topics and keywords that most frequently appear in financial risk assessment
research (RM2), thereby identifying the main themes that are the focus of
research across various industrial sectors.
This study will also analyze the distribution of articles
based on the financial risk assessment methods and approaches used (RM3), such
as Analytic Hierarchy Process (AHP), fuzzy logic, Monte Carlo
Simulation, Value at Risk (VaR), Conditional Value at Risk (CVaR),
predictive analytics, and various other quantitative modeling
approaches. In addition, this study will identify the sectors or research areas
most discussed in financial risk assessment studies (RM4), including
healthcare, tourism, supply chain finance, internet finance, renewable energy,
infrastructure projects, and sustainability finance.
Furthermore, this study will also examine how financial
risk assessment is used to support organizational decision-making and risk
management in modern enterprises (RM5). This study will examine how financial
risk assessment helps organizations improve the effectiveness of risk control,
maintain financial stability, and support business sustainability across
various industrial sectors. Furthermore, this study will identify key factors
influencing the development of Financial Risk Assessment Management in
modern organizations (RM6), including digital transformation, financial
technology developments, sustainability finance, quantitative modeling, and
enterprise risk management.
Thus, this study aims to provide a more comprehensive
understanding of the development, implementation, and direction of Financial
Risk Assessment Management research from a management perspective over the
past 10 years.
METHOD
In this study, the primary data sources used were
international scientific articles published in the Scopus database within the
last 10 years, from 2016 to 2025. These articles were selected for their
relevance to Financial Risk Assessment Management from a management
perspective. The literature used includes research on enterprise risk
management, financial risk assessment, sustainability finance, supply chain
finance, internet finance, quantitative modeling, and various modern financial
risk management approaches (Li et al., 2018) (Brunner-kirchmair, 2019) (Yan & Wang, 2020). All articles were from international journals indexed
by Scopus and selected based on research quality, topic relevance, and
theoretical and practical contributions to the development of financial risk
management (İşletmelerinde &
Risklerin, 2021) (Wang, 2025).
The method used in this study is a Systematic
Literature Review (SLR) following the PRISMA 2020 approach (Preferred
Reporting Items for Systematic Reviews and Meta-Analyses). The SLR method
is used to systematically identify, evaluate, and synthesize research to
provide a comprehensive overview of the development of Financial Risk
Assessment Management research (Tranfield et
al., 2003). This approach was chosen because it can help
researchers understand the development of methods, the implementation of
financial risk assessment, and research trends in various industrial sectors (Kitchenham, 2004).
The article search was conducted in the Scopus database
using the keywords "financial risk assessment" and "enterprise
risk management." The identification stage yielded 79 articles related
to the research topic. Next, a screening process was conducted based on
the suitability of the title, abstract, year of publication, and relevance to
the management field. Articles that were irrelevant, duplicate, or not in line
with the research focus were eliminated at this stage (Moher et al., 2009) (Liberati et al., 2009).
The next stage is eligibility, which is the
process of evaluating the suitability of articles based on research content,
methods used, research contributions, and their relevance to Financial Risk
Assessment Management. After the evaluation process, 25 international
articles met the inclusion criteria and were used as primary sources in this
study. This approach was taken to ensure that the articles used were of good
research quality and relevant to a management perspective (Page et al., 2021).
Data from the selected articles were then analyzed using
a descriptive approach and literature synthesis. The analysis was conducted to
identify developments in financial risk assessment methods, the most researched
industrial sectors, risk management approaches used, and research trends over
the past 10 years (Kumar et al., 2017) (Franzoni et al., 2019) (Zhao, 2024). Furthermore, this study analyzes the development of
financial risk assessment to support organizational decision-making, corporate
risk control, sustainability finance, and enterprise risk management in modern
organizations (Martínez-ruiz et al.,
2023) (Wang, 2025) (Su & Zhou, 2018).
Systematic
Literature Search
The development of globalization, digital transformation,
and increasing economic uncertainty have caused organizations to face
increasingly complex financial risks, making Financial Risk Assessment
Management a crucial part of modern management systems (Li et al., 2018) (Brunner-kirchmair, 2019) (Yan & Wang, 2020). Financial risk assessment is not only used to identify
and control a company's financial risks but also supports strategic
decision-making, maintains organizational stability, and improves business
sustainability across various industrial sectors (İşletmelerinde &
Risklerin, 2021)(Franzoni et al., 2019) (ZHYKHARIEVA1 et al.,
2025). This study aims to systematically review the
development of research on Financial Risk Assessment Management from a
management perspective over the past 10 years.
This study explores various financial risk assessment
methods such as Analytic Hierarchy Process (AHP), fuzzy logic, Monte
Carlo Simulation, Value at Risk (VaR), Conditional Value at Risk (CVaR),
predictive analytics, and quantitative modeling used in various industrial
sectors (Kumar et al., 2017) (Yang & Estimation,
2024) (Su & Zhou, 2018). Furthermore, this study also identifies the
implementation of financial risk assessment in the healthcare, tourism, supply
chain finance, internet finance, renewable energy, and sustainability finance
sectors (Su & Zhou, 2018) (İşletmelerinde &
Risklerin, 2021)(Wang, 2025). Using a Systematic Literature Review (SLR)
approach, this study collects and analyzes prior studies to provide a more
comprehensive understanding of the development of methods, implementation, and
research directions in Financial Risk Assessment Management.
The Scopus database was used because it has a broad,
structured, and reliable coverage of international scientific literature for
SLR-based research. The article search was conducted in Scopus using the
keywords "financial risk assessment" and "enterprise
risk management" in the title, abstract, and keywords
sections. To facilitate the identification and screening of articles,
researchers used the Web AI platform Watase as an AI-assisted literature
search tool to support the search, management, and organization of research
data more systematically. However, all primary data sources in this study still
come from the Scopus database.
The data collection stages were carried out by: (1)
opening the Scopus database via Scopus.com; (2) entering search keywords; (3)
limiting the type of document to journal articles only ; (4) determining
the range of publication years for the last 10 years, starting from 2016 to
2025. This year was chosen based on the number of studies that began to
research the topic, and 2026 was not selected because the year was still
ongoing; and (5) conducting the article search process in the Scopus database.
Based on the initial search results, 79 articles were obtained related to the
research topic.
The obtained data were then stored in CSV and RIS formats
to facilitate reference management and data analysis. A screening process
was then conducted using inclusion and exclusion criteria based on the
suitability of the title and abstract, the year of publication, the research's
relevance to the management field, and the article's relationship to the topic
of Financial Risk Assessment Management. Articles that were irrelevant,
duplicate, or not in line with the research focus were excluded, leaving 25
international articles that met the inclusion criteria and served as the
primary sources for the research.
The research used the PRISMA 2020 (Preferred Reporting
Items for Systematic Reviews and Meta-Analyses) approach, which comprises
the identification, screening, eligibility, and inclusion stages (Moher et al., 2009) (Liberati et al., 2009). This approach was used to ensure that the article
selection process was carried out systematically, transparently, and in a
structured manner, thereby improving the validity and quality of the research
results (Tranfield et al., 2003) (Kitchenham, 2004).
RESULTS
AND DISCUSSION
Trends
in Financial Risk Assessment Management Research Publications in the Last 10
Years
To gain a broader overview of the development of research
in Financial Risk Assessment Management, this study analyzes publication
trends over the past 10 years using the Scopus database. Publication trend
analysis is important for observing research developments, changes in focus,
and the growing attention to financial risk assessment from a management
perspective.
Based on data collection results, research on Financial
Risk Assessment Management has shown significant progress in recent years.
In the early stages of research, publications on financial risk assessment were
relatively few, with most focusing on traditional approaches such as financial
ratio analysis, the Analytic Hierarchy Process (AHP), and fuzzy
logic. However, with the development of digital transformation,
sustainability finance, and quantitative modeling, the number of studies on
financial risk assessment has been increasing.
The increase in publications became evident after 2018,
when various studies began discussing financial risk assessment across internet
finance, supply chain finance, renewable energy, and sustainability management.
Furthermore, the development of digital technology and the increasing use of
predictive analytics have driven research on machine learning, deep learning,
anomaly detection, and dynamic risk assessment in organizational financial risk
management.
Based on the analysis, the highest increase in
publications occurred in the period following the COVID-19 pandemic. This
situation indicates that global economic uncertainty, changes in the business
environment, and rising financial risks have heightened the need for more
adaptive, rapid, and integrated financial risk assessment systems for
organizations. Furthermore, recent research indicates a shift in focus from
traditional financial risk assessment approaches to predictive modeling,
sustainability finance, and enterprise risk management. This increase can be
seen in the following figure:
Figure 1. Publication trend graph
Relationship
Between Keywords (Research Topics)
Figure 2. Word Cloud (Keyword Frequency Map)
A keyword relationship analysis was conducted to identify
the most frequently occurring research themes in Financial Risk Assessment
Management studies. This visualization illustrates the interrelationships
among research topics, keyword frequency, and the direction of research
development, based on articles that have passed the screening process.
In this visualization, the font size indicates the
frequency of a keyword's appearance in the analyzed literature. The larger the
font size of a keyword, the more frequently it appears in research. Conversely,
keywords with smaller font sizes indicate that the topic is relatively limited
or under-researched. Furthermore, keywords positioned in the center of the
visualization indicate a stronger connection to other research topics, while
keywords located at the edges generally have a more specific relationship to a
particular theme.
Based on the visualization results in Figure 2, the
keyword "financial risk assessment" is the most dominant topic
because it has the largest font size and is located in the center of the
visualization. This finding indicates that financial risk assessment is the
primary focus of the analyzed research. Furthermore, several other keywords
with high frequency of occurrence are "risk assessment,"
"financial risk," "risk management," "supply chain
risk management," and "supply chain finance." These keywords form
the core of research on the evaluation and management of financial risk across
various organizational and industrial sectors.
The visualization also shows a strong correlation between
financial risk assessment and various analytical methods, such as the analytic
hierarchy process, Monte Carlo simulation, machine learning, deep learning,
artificial intelligence, graph neural networks, feature selection, and credit
risk assessment. This demonstrates that financial risk assessment research
continues to evolve, utilizing quantitative approaches and more complex
analytical techniques to improve the accuracy of financial risk measurement.
In addition to the methodological aspects, several
keywords related to the research object also appear quite dominant, such as
internet finance, listed tourism companies, renewable energy, biofuels,
shipping companies, marine engineering, healthcare, and cloud accounting. These
findings indicate that the application of financial risk assessment is not
limited to the traditional financial sector but has expanded to various
industries with high levels of uncertainty and financial risk.
Overall, the visualization results show that research in Financial
Risk Assessment Management has experienced quite rapid development in
recent years. The focus of research is not only on financial risk measurement
but also on the development of analytical methods, managerial decision-making,
supply chain risk management, business sustainability, and risk evaluation in
increasingly complex industrial sectors. Thus, financial risk assessment has
become a central theme in supporting the effectiveness of risk management and
strategic decision-making in modern organizations.
Distribution article based on Method
Study
Metadata processing (CSV) also helps
provide answers about the method used in each article. Type method which is
intended to be presented in the following image:
Figure 3. Research
Methods for Each Article
Based on Figure 3, the most frequently used research
method was quantitative, with 13 articles (50%). Furthermore, experimental
model methods were used in 10 articles (42.3%). Meanwhile, conceptual,
qualitative, and mixed methods were each used in only one article (3.8%). These
findings indicate that research in Financial Risk Assessment Management is
dominated by quantitative approaches and model development, reflecting the high
demand for data-driven financial risk measurement, prediction, and evaluation to
support modern organizational decision-making.
Distribution article based on Object
Study
In addition to the distribution of research methods, the
analysis of article data also provides information on the research objects used
in each study. Generally, research objects in the study of Financial Risk
Assessment Management are grouped into two categories: business and non-business.
The business category includes companies and various industrial sectors such as
finance, healthcare, tourism, energy, logistics, supply chains, and state-owned
enterprises. Meanwhile, the non-business category includes research focused on
developing models, algorithms, conceptual frameworks, and methods for financial
risk analysis without a specific industry objective. This division helps
explain research trends in the application and development of Financial Risk
Assessment Management methods.
Figure 4. Object Research For Each Article
In addition to the distribution of research methods, the
analysis of the 25 selected articles also provides information on the research
objects used in the Financial Risk Assessment Management study. Based on
Figure 4, the research objects are dominated by business or industrial
fields, accounting for 84% (21 articles). Meanwhile, research
objects that fall into the non-business or conceptual category are only 16%,
or 4 articles.
The predominance of research subjects in the business
sector indicates that Financial Risk Assessment Management research is
more focused on solving real-world problems faced by organizations and
companies. The various industrial sectors covered include supply chain
finance, internet finance, healthcare organizations, tourism
companies, the hospitality industry, marine enterprises, port
enterprises, renewable energy, biofuel supply chains, and state-owned
enterprises (SOEs). These findings demonstrate that financial risk
management has become a critical requirement for supporting business
continuity, improving decision-making quality, and minimizing potential
organizational losses.
On the other hand, research with non-business or
conceptual objectives is still relatively limited. This research group
generally focuses on developing models, algorithms, conceptual frameworks, and
analytical methods to improve the accuracy and effectiveness of the financial
risk assessment process, without being specifically applied to any industry
sector. Although fewer in number, research in this category has made
significant contributions to the development of theories, methods, and
innovations that underpin the application of financial risk assessment across
various business sectors.
Overall, these results indicate that research in Financial
Risk Assessment Management over the past 10 years has focused more on
practical business applications than on conceptual development. This indicates
that organizations' need for effective financial risk management systems
continues to grow in line with the growing complexity of the business
environment and global economic uncertainty.
Table 1. Classification method And object
study
|
No |
Article Code |
Year |
Researchers |
Method |
Object |
|||
|
Quantitative |
Qualitative |
Mixture |
Business |
Non-Business |
||||
|
1 |
M1 |
2018 |
(Li
et al., 2018) |
1 |
|
|
1 |
|
|
2 |
M2 |
2017 |
(Kumar
et al., 2017) |
1 |
|
|
1 |
|
|
3 |
M3 |
2019 |
(Brunner-kirchmair,
2019)B |
|
1 |
|
|
1 |
|
4 |
M4 |
2019 |
(Franzoni
et al., 2019) |
1 |
|
|
1 |
|
|
5 |
M5 |
2018 |
(Su
& Zhou, 2018) |
1 |
|
|
1 |
|
|
6 |
M6 |
2020 |
(Yan
& Wang, 2020) |
1 |
|
|
1 |
|
|
7 |
M7 |
2025 |
(Liu
et al., 2025) |
|
|
1 |
1 |
|
|
8 |
M8 |
2023 |
(Fan
et al., 2023) |
1 |
|
|
1 |
|
|
9 |
M9 |
2024 |
(Yang
& Estimation, 2024) |
|
|
1 |
1 |
|
|
10 |
M10 |
2021 |
(İşletmelerinde
& Risklerin, 2021) |
|
|
1 |
1 |
|
|
11 |
M11 |
2022 |
(Elston,
2022) |
|
|
1 |
1 |
|
|
12 |
M12 |
2022 |
(Chi
et al., 2022) |
1 |
|
|
1 |
|
|
13 |
M13 |
2022 |
(Lin
et al., 2022) |
|
|
1 |
1 |
|
|
14 |
M14 |
2022 |
(Andaloro
et al., 2022) |
1 |
|
|
1 |
|
|
15 |
M15 |
2023 |
(Zou,
2024). |
1 |
|
|
1 |
|
|
16 |
M16 |
2023 |
(Hirsa
& Lin, 2020) |
|
|
1 |
|
1 |
|
17 |
M17 |
2016 |
(Santibañez-aguilar
et al., 2016) |
|
|
1 |
|
1 |
|
18 |
M18 |
2016 |
(Santibañez-aguilar
et al., 2016) |
1 |
|
|
1 |
|
|
19 |
M19 |
2025 |
(Wang,
2025) |
1 |
|
|
1 |
|
|
20 |
M20 |
2025 |
(Kondoju
& Bindewari, 2025) |
|
|
1 |
1 |
|
|
21 |
M21 |
2025 |
(Naveed
et al., 2025) |
|
|
1 |
|
1 |
|
22 |
M22 |
2025 |
(Liu
et al., 2025) |
|
|
1 |
|
1 |
|
23 |
M23 |
2025 |
(ZHYKHARIEVA1
et al., 2025) |
|
|
1 |
|
1 |
|
24 |
M24 |
2022 |
(Bingler
et al., 2022) |
|
1 |
|
1 |
|
|
25 |
M25 |
2023 |
(Lu
et al., 2023) |
1 |
|
|
1 |
|
Table 2. Classification Researchers, Title, Variables And Results Study
|
NO |
Year |
Researchers |
Title |
Variables |
Research result |
|
1 |
2018 |
(Li et al., 2018) |
A Novel
Financial Risk Assessment Model for Companies Based on Heterogeneous
Information and Aggregated Historical Data |
Independent:
Heterogeneous Information; Dependent:
Financial Risk Assessment; Supporting: Historical Aggregated Data |
The study
developed a financial risk assessment model based on heterogeneous
information and historical company data. The results showed that integrating
multiple information sources improved the accuracy of risk identification
compared to conventional approaches and helped detect potential financial
distress earlier. |
|
2 |
2018 |
(Kumar et al., 2017) |
Financial Risk
Assessment and Modeling of PPP Based Indian Highway Infrastructure Projects |
Independent:
Project Risk; Dependent: Financial Risk Assessment; Moderation: Monte Carlo
Simulation |
Monte Carlo
simulation has proven effective in evaluating uncertainty in infrastructure
project investments. The developed model can more accurately estimate risk
levels and help investors mitigate potential investment losses. |
|
3 |
2019 |
(Brunner-kirchmair, 2019) |
Knowledge is
Power – Conceptualizing Collaborative Financial Risk Assessment |
Independent:
Collaborative Knowledge; Dependent:
Financial Risk Assessment |
Collaboration
and knowledge exchange between stakeholders improves the quality of the
financial risk assessment process. A collaborative approach results in more
effective and comprehensive risk management decisions. |
|
4 |
2019 |
(Franzoni et al., 2019) |
Rainfall
Financial Risk Assessment in the Hospitality Industry |
Independent:
Rainfall Intensity; Dependent:
Hospitality Financial Risk |
Weather factors
such as rainfall impact the financial health of the hospitality industry.
External risks have been shown to impact customer visitation rates and
company revenue. |
|
5 |
2018 |
(Su & Zhou, 2018) |
Financial Risk
Assessment of Listed Tourism Companies Based on Gray Relational Degree Model |
Independent:
Financial Indicators; Dependent:
Financial Risk Assessment |
The Grey
Relational Degree Model can effectively identify the risk level of tourism
companies. This model can be used as a basis for management and investment
decisions. |
|
6 |
2020 |
(Yan & Wang, 2020) |
Research on
Risk Assessment and Control of Internet Finance in China Based on FAHP |
Independent:
Internet Finance Risk; Dependent: Risk
Control; Supporter: FAHP |
The FAHP method
can improve the effectiveness of risk evaluation and control in the internet
finance sector. The model helps organizations determine more appropriate risk
mitigation strategies. |
|
7 |
2025 |
(Liu et al., 2025) |
Financial Risk
Assessment and Pre-warning of Port Enterprises Based on Neural Network Model |
Independent:
Neural Network Model; Dependent:
Financial Risk Assessment |
A neural
network can detect potential financial risks in port companies earlier. The
developed early warning system helps organizations reduce the likelihood of
financial distress. |
|
8 |
2023 |
(Fan et al., 2023) |
Financial Risk
Assessment of Marine Enterprises Based on Analytic Hierarchy Process |
Independent:
AHP; Dependent: Financial Risk Assessment |
AHP helps
maritime companies systematically evaluate and prioritize financial risks,
thereby increasing the effectiveness of risk management decision-making. |
|
9 |
2024 |
(Yang & Estimation, 2024) |
Research on
Supply Chain Financial Risk Assessment Based on Blockchain and Fuzzy Neural
Networks |
Independent:
Blockchain Technology; Dependent:
Supply Chain Financial Risk |
Blockchain
increases data transparency in supply chain finance, while fuzzy neural
networks enhance the system's ability to identify and predict financial
risks. |
|
10 |
2021 |
(İşletmelerinde & Risklerin, 2021) |
Financial Risk
Assessment in Healthcare Organizations |
Independent:
Procurement Risk; Dependent:
Healthcare Financial Risk |
Financial risk
assessment helps healthcare organizations control procurement, liquidity, and
resource utilization risks, thereby improving the organization's operational
stability. |
|
11 |
2022 |
(Elston, 2022) |
Financial Risk
Assessment to Improve the Accuracy of Financial Prediction in the Internet
Financial Industry Using Data Analytics Models |
Independent:
Data Analytics; Dependent: Financial Prediction Accuracy |
Data analytics,
machine learning, and deep learning are improving the accuracy of financial
risk predictions in the internet finance industry. Models are able to
identify risk patterns that are difficult to detect with traditional methods. |
|
12 |
2022 |
(Chi et al., 2022) |
Financial Risk
Assessment of Photovoltaic Industry Listed Companies Based on Text Mining |
Independent:
Text Mining; Dependent: Financial Risk Assessment |
Text mining is
able to extract important information from company documents and support
early warning systems for financial risks more quickly and accurately. |
|
13 |
2022 |
(Lin et al., 2022) |
Financial Risk
Assessment of Enterprise Management Accounting Based on Association Rule
Algorithm under the Background of Big Data |
Independent:
Big Data; Dependent: Financial Risk Assessment |
Association
Rule Algorithm in big data environment increases the effectiveness of
financial risk evaluation and helps to understand the relationship between
company financial indicators. |
|
14 |
2022 |
(Andaloro et al., 2022) |
De-Risking the
Energy Efficient Renovation of Commercial Office Buildings through
Technical-Financial Risk Assessment |
Independent:
Technical Risk; Dependent: Financial Risk Assessment |
Integration of
technical and financial aspects can reduce investment uncertainty in
energy-efficient building projects and improve the quality of investment
decision-making. |
|
15 |
2024 |
(Zou, 2024) |
Financial Risk
Assessment Management of State-Owned Enterprises Based on Cloud Accounting in
the Era of Big Data |
Independent:
Big Data; Dependent: Financial Risk Management |
Cloud
accounting and big data increase the effectiveness of financial risk
management and provide faster and more accurate information for management. |
|
16 |
2020 |
(Hirsa & Lin, 2020) |
Explainable AI
in Credit Risk Management |
Independent:
Explainable AI; Dependent: Credit Risk Management |
Explainable AI
improves the transparency and interpretability of credit risk assessment
models, thereby increasing user confidence in the prediction results. |
|
17 |
2016 |
(Santibañez-aguilar et al., 2016) |
Financial Risk
Assessment and Optimal Planning of Biofuels Supply Chains under Uncertainty |
Independent:
Supply Chain Uncertainty; Dependent:
Financial Risk Assessment |
Biofuel supply
chain uncertainty significantly impacts financial risk. The developed model
helps organizations determine optimal operational strategies. |
|
18 |
2024 |
(Zhao, 2024) |
Financial Risk
Assessment Management of Cloud Accounting SOEs Based on Spillover Index |
Independent:
Spillover Index; Dependent:
Financial Risk Management |
The spillover
index helps identify the relationship and distribution of risks between
sectors, thereby increasing the effectiveness of a company's risk mitigation
strategy. |
|
19 |
2025 |
(Wang, 2025) |
Credit Risk
Assessment of Green Supply Chain Finance for SMEs Based on Multi-Source
Information Fusion |
Independent:
Multi-Source Information Fusion; Dependent: Credit Risk Assessment |
Integration of
various information sources improves the accuracy of credit risk assessments
in MSMEs and supports more appropriate financing decision-making. |
|
20 |
2025 |
(Kondoju & Bindewari, 2025) |
Leveraging AI
for Dynamic Risk Assessment in Financial Services |
Independent:
Dynamic Risk Assessment; Dependent:
Financial Service Risk |
Dynamic risk
assessment enables organizations to conduct real-time risk evaluations,
thereby improving their ability to respond to changing market conditions. |
|
21 |
2025 |
(Naveed et al., 2025) |
Financial
Modeling System Using Deep Neural Networks (DNNs) for Financial Risk
Assessments |
Independent:
Deep Neural Network; Dependent:
Financial Risk Assessment |
Deep Neural
Networks produce higher risk prediction accuracy than conventional methods
and are capable of processing complex financial data. |
|
22 |
2025 |
(Liu et al., 2025) |
Supply Chain
Financial Risk Assessment: A Modified Graph Attention Neural Network |
Independent:
Graph Attention Neural Network; Dependent: Supply Chain Financial Risk |
Graph Attention
Neural Network improves the system's ability to identify and predict supply
chain financial risks with a higher degree of accuracy. |
|
23 |
2025 |
(ZHYKHARIEVA1
et al., 2025) |
Financial Risk
Assessment Based on a Dynamic Model: A Case of Shipping Investments |
Independent
Variables: Freight Rates, Operating Expenditure (OPEX), Financing Conditions,
Debt Structure, Investment Cost. Dependent Variable: Financial Risk
Assessment. Supporting Variables: Dynamic Financial Model, Scenario Analysis,
DCF, NPV, IRR.
|
Independent
Variables: Freight Rates, Operating Expenditure (OPEX), Financing Conditions,
Debt Structure, Investment Cost. Dependent Variable: Financial Risk
Assessment. Supporting Variables: Dynamic Financial Model, Scenario Analysis,
DCF, NPV, IRR. This study developed a dynamic financial model
(three-statement financial model) that integrates the income statement,
balance sheet, and cash flow to assess the financial risk of shipping
investments. The results show that freight rate volatility, operating costs,
and the financing structure are the main factors influencing investment risk.
The scenario analysis yielded 27 possible conditions with varying DCF values.
The project risk is classified as high with a coefficient of variation of
56%, while the probability of a negative DCF is 2.3%. This model is effective
for supporting investment decisions, financing, and risk mitigation
strategies in shipping companies. |
|
24 |
2022 |
(Bingler
et al., 2022) |
Taming the
Green Swan: A Criteria-Based Analysis to Improve the Understanding of
Climate-Related Financial Risk Assessment Tools |
Independent
Variables:
Accountability, Depth of Risk Analysis, and Usability. Dependent Variable:
Quality of Climate-Related Financial Risk Assessment Tools. Indicators:
Model transparency, emission data strategy, scientific foundation, hazard
coverage, exposure, vulnerability and resilience, adaptability, economic
impact, risk amplification, output interpretability, and uncertainty
management. |
The study
developed a criteria-based framework to evaluate the quality of
climate-related financial risk assessment tools. An assessment of 16 climate
transition risk tools revealed that most tools demonstrated moderate
performance, while only a few achieved high performance across all evaluation
criteria. The findings indicate that model transparency, scenario
flexibility, assumption communication, and uncertainty management remain the
main weaknesses of existing tools. Significant variation was also observed in
the depth of risk analysis performed by different tools. The study recommends
improving methodological transparency, adopting standardized reporting
templates, and presenting risk outcomes through probability distributions or
confidence intervals to enhance comparability, interpretability, and decision
usefulness for regulators, investors, and other stakeholders |
|
25 |
2023 |
(Lu
et al., 2023) |
Profit vs.
Equality? The Case of Financial Risk Assessment and A New Perspective on
Alternative Data |
Independent
Variables: Alternative Data. Dependent Variables: Credit Risk Assessment,
Financial Inclusion, and Profitability |
Alternative
data, particularly smartphone activity data, improve credit risk assessment
accuracy, increase financial inclusion, and enhance lending profitability
while reducing potential bias in credit decisions. |
Relationships
Between Topics in Financial Risk Assessment Management
Overall, the analyzed research indicates that Financial
Risk Assessment Management is a developing, multidisciplinary field of
study encompassing various aspects, from financial risk measurement and
organizational risk management to decision-making and analytical technologies,
and its application across various industrial sectors. The study results
indicate that financial risk assessment serves not only as a risk evaluation
tool but also as a basis for developing organizational strategies and improving
business sustainability. Some key relationships that can be identified are as
follows:
1.
Financial Risk Assessment and Risk Management
Most studies show that financial risk assessment is
closely related to risk management. Financial risk assessment is used to
identify, measure, and control various risks that can impact organizational
performance (İşletmelerinde &
Risklerin, 2021) (Yan & Wang, 2020) (Wang, 2025). In the healthcare, state-owned enterprise, tourism,
maritime, and energy sectors, financial risk assessment has proven to be a
crucial component in supporting managerial decision-making and sustainable
organizational risk management (Su & Zhou, 2018) (Zou, 2024) (Andaloro et al., 2022).
2.
Financial Risk Assessment and Decision Making
Various studies have shown that financial risk assessment
plays a crucial role in supporting an organization's strategic decision-making
process. The Grey Relational Degree Model, Monte Carlo Simulation,
and the Spillover Index are used to help organizations determine the
most optimal decision alternatives when facing uncertain business environments(Kumar et al., 2017) (Liu et al., 2025). The better the financial risk assessment process, the
more effective the organization's decisions in managing its resources and
investments.
3.
Financial Risk Assessment and Analytical Technology
Technological developments have driven a shift in the
approach to financial risk assessment, moving from traditional methods to
analytics- and data-modeling-based approaches. Several studies have
demonstrated the use of machine learning, deep learning, graph
neural networks, anomaly detection, text mining, and predictive
analytics to improve the accuracy of financial risk identification and
prediction (Kondoju & Bindewari,
2025) (Liu et al., 2025) (Li et al., 2018). Furthermore, explainable AI has been used to increase
the transparency of credit risk prediction results, making them easier for
users to understand (Santibañez-aguilar et
al., 2016).
4.
Financial Risk Assessment and Supply Chain Finance
Research on supply chain finance shows that
financial risk assessment helps reduce financing uncertainty, improve the
quality of credit evaluations, and strengthen relationships among supply chain
actors. The use of blockchain and fuzzy neural networks has been shown to
enhance transparency and effectiveness in supply chain financial risk
management (Yang & Estimation,
2024). Furthermore, integrating multi-source information
fusion and graph attention neural networks also improves the
accuracy of credit risk assessments and risk identification in supply chain
finance (Wang, 2025) (Zhao, 2024). Research by (Zhao, 2024) also shows that financial risk assessment helps
organizations navigate uncertainty in the biofuel supply chain more
effectively.
5.
Financial Risk Assessment and Sustainability
The study results indicate a strong relationship between
financial risk assessment and sustainability aspects. Research in
renewable energy, biofuels, building energy efficiency, and green supply chain
finance indicates that financial risk evaluation is a crucial factor in
supporting sustainable investment (Wang, 2025) (Andaloro et al., 2022). These findings demonstrate that financial risk
assessment is not solely focused on economic returns but also considers
environmental factors and long-term sustainability in the investment
decision-making process.
Key
Factors in Financial Risk Assessment Management
1.
Data and Information Quality
The availability of accurate and relevant data is a key
factor in generating accurate risk assessments. The use of heterogeneous
information, historical aggregated data, and multi-source
information fusion has been shown to improve the quality of corporate
financial risk predictions (Li et al., 2018) (Zhao, 2024).
2.
Analysis Methods and Models
The selection of an appropriate analysis method
significantly determines the outcome of a risk evaluation. Various methods such
as FAHP, Grey Relational Degree Model, Monte Carlo Simulation,
Association Rule Algorithm, and Neural Network have been used to
improve the accuracy of financial risk measurement (Kumar et al., 2017) (Su & Zhou, 2018) (Yan & Wang, 2020) (Fakhravar, 2020).
3.
Digital Technology and Big Data
Digital transformation encourages organizations to use big
data, cloud accounting, predictive analytics, and financial
modeling in their financial risk management processes (Liu et al., 2025) (Zhao, 2024). The use of digital technology has been shown to
increase the speed, accuracy, and effectiveness of organizational
decision-making.
4.
Industry Characteristics
Each industrial sector has distinct risk characteristics.
Research shows that financial risk assessment approaches have been applied to
the healthcare, tourism, ports, maritime, energy, supply chain, hospitality,
and internet finance sectors (Franzoni et al., 2019) (Su & Zhou, 2018) (Yan & Wang, 2020) (İşletmelerinde &
Risklerin, 2021). Therefore, risk evaluation methods need to be tailored
to the conditions of each industrial sector.
5.
Uncertainty in the Business Environment
Changes in economic conditions, technology, weather, and
market dynamics are factors that influence an organization's risk level.
Research across the hospitality, infrastructure, supply chain, and energy
sectors shows that financial risk assessments help organizations better
understand and anticipate uncertain business environments (Franzoni et al., 2019)(Kumar et al., 2017) (Zhao, 2024).
6.
Sustainability and Long-Term Investment
Recent research shows that sustainability is increasingly
becoming a focus in financial risk assessments. Organizations are beginning to
consider environmental, energy, and sustainability risks in their investment
decision-making processes (Andaloro et al., 2022) (Kondoju & Bindewari,
2025). These findings suggest that modern financial risk
assessments focus not only on profitability but also on the organization's
long-term sustainability.
Overall, the research results show that Financial Risk
Assessment Management is evolving from a conventional financial risk
measurement approach to a more integrated, adaptive, and strategic
decision-making-oriented risk management system for the organization.
CONCLUSION
After conducting a Systematic Literature Review (SLR) of
25 international articles on Financial Risk Assessment Management, it
can be concluded that financial risk assessment has become a strategic
component of modern organizational management. Financial risk assessment is no
longer only used to identify and measure financial risks; it also serves as a
basis for decision-making, business strategy development, resource allocation,
and increased organizational resilience in the face of uncertain business
environments. Research indicates that the effectiveness of financial risk
assessment is influenced by data and information quality, the selection of
appropriate analytical methods, industry characteristics, and the
organization's ability to manage risks in an integrated manner.
The study also shows a shift in approach from traditional
financial risk assessment methods to a more comprehensive, technology-based
approach. While various methods such as the Analytic Hierarchy Process (AHP),
the Grey Relational Degree Model, Monte Carlo Simulation, and the
Fuzzy Analytic Hierarchy Process (FAHP) are still used to support risk
evaluation, recent research has shown an increasing use of analytical
technology, data modeling, and predictive systems that are capable of producing
higher levels of accuracy. These developments indicate that organizations
increasingly need risk assessment systems that are adaptive, responsive, and
capable of supporting rapid decision-making in a dynamic business environment.
Furthermore, the analyzed research shows that Financial
Risk Assessment Management has been implemented across sectors such as
healthcare, tourism, ports, maritime, supply chains, renewable energy,
state-owned enterprises, and even the digital financial sector. These findings
demonstrate that financial risk is a challenge faced by almost all
organizations, necessitating a risk management approach that adapts to the
characteristics and needs of each sector. Furthermore, the increasing attention
to sustainability aspects indicates that financial risk assessment is not
solely oriented towards achieving economic profit but also considers the
long-term impact on organizational stability and business sustainability.
Overall, this research demonstrates that Financial
Risk Assessment Management plays a crucial role in supporting the success
of modern organizations. An organization's ability to effectively identify,
evaluate, and manage financial risks will determine the quality of decisions
made, its resilience to environmental changes, and its ability to create
sustainable value. Therefore, developing a more integrated, accurate, and
needs-oriented financial risk assessment system is crucial for addressing
future business challenges.
Reference
Andaloro, A.,
Salvalai, G., Fregonese, G., Tso, L., & Paoletti, G. (2022). De-Risking
the Energy Efficient Renovation of Commercial Office Buildings through
Technical-Financial Risk Assessment. 1–20.
Bingler, J. A.,
Senni, C. C., & Bingler, J. A. (2022). Taming the Green Swan : a
criteria-based analysis to improve the understanding of climate-related
financial risk assessment tools understanding of climate-related fi nancial
risk assessment tools. https://doi.org/10.1080/14693062.2022.2032569
Brunner-kirchmair,
T. M. (2019). Knowledge is power – conceptualizing collaborative fi nancial
risk assessment. https://doi.org/10.1108/JRF-05-2018-0083
Chi, Y., Yan, M.,
Pang, Y., & Lei, H. (2022). Financial Risk Assessment of Photovoltaic
Industry Listed Companies Based on Text Mining.
Elston, F. (2022).
Financial risk assessment to improve the accuracy of financial prediction in
the internet financial industry using data analytics models. Operations
Management Research, 925–940. https://doi.org/10.1007/s12063-022-00293-5
Fakhravar, H.
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