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Machine Learning Based Risk Management in Finance and Insurance

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A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Technology and Innovation".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 10225

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Special Issue Editors

Dr. Mohamed Chaouch

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Guest Editor

Department of Mathematics, Statistics and Physics, Qatar University, Doha 2713, Qatar
Interests: financial econometrics; statistical learning for big data; copula-based modeling; functional data analysis

Prof. Dr. Thanasis Stengos

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Guest Editor

Department of Economics and Finance, University of Guelph, Guelph, ON N1G2W1, Canada
Interests: nonparametric econometrics; applied time series models; empirical finance; empirical growth
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Special Issue Information

Dear Colleagues,

The finance and insurance industries are increasingly facing a rapidly changing landscape characterized by increased uncertainty, complex interdependencies, and the proliferation of high-dimensional and big data. Traditional risk management methods, while basic, are often challenged by these complexities, necessitating the integration of more sophisticated statistical and machine learning techniques. This Special Issue aims to cover the cutting edge of research and innovation in applying these advanced methodologies to quantitative risk management in finance and insurance.

We invite submissions that address a wide range of topics, including, but not limited to, portfolio optimization, credit risk modeling, insurance pricing, catastrophe modeling, systemic risk analysis, fraud detection, and algorithmic trading. Contributions to developing new models, improving existing techniques, or innovative applications of machine learning algorithms (e.g., deep learning, ensemble methods, and reinforcement learning) are strongly encouraged. In addition, we welcome research that addresses the challenges of model interpretation, robustness, and scalability and research that examines the ethical implications and regulatory aspects of implementing these techniques in real-world scenarios. This Special Issue also highlights interdisciplinary approaches that bridge the gap between finance, insurance, statistics, and computer science, providing new perspectives on managing and mitigating risk in an increasingly complex environment. Particular attention will be given to submissions that provide empirical evidence through case studies or propose methods/algorithms that combine machine learning with traditional risk management practices. By bringing together cutting-edge research and practical insights, this Special Issue aims to advance the field and serve as a critical resource for academics, industry professionals, and policymakers striving to improve risk management strategies in an era of unprecedented change.

Dr. Mohamed Chaouch
Prof. Dr. Thanasis Stengos
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

quantitative risk management
financial risk
insurance risk
machine learning
statistical modeling
portfolio optimization
credit risk modeling
insurance pricing
catastrophe modeling
systemic risk
predictive analytics
deep learning
ensemble methods
reinforcement learning
fraud detection
algorithmic trading
model interpretability
regulatory compliance
high-dimensional data
model robustness
risk mitigation
ethical AI in finance

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Published Papers (11 papers)

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Research

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22 pages, 1294 KiB

Open AccessArticle

Variational Autoencoders for Completing the Volatility Surfaces

by Bienvenue Feugang Nteumagné, Hermann Azemtsa Donfack and Celestin Wafo Soh

J. Risk Financial Manag. 2025, 18(5), 239; https://doi.org/10.3390/jrfm18050239 - 30 Apr 2025

Abstract

Variational autoencoders (VAEs) have emerged as a promising tool for modeling volatility surfaces, with particular significance for generating synthetic implied volatility scenarios that enhance risk management capabilities. This study evaluates VAE performance using synthetic volatility surfaces, chosen specifically for their arbitrage-free properties and clean data characteristics. Through a comprehensive comparison with traditional methods including thin-plate spline interpolation, parametric models (SABR and SVI), and deterministic autoencoders, we demonstrate that our VAE approach with latent space optimization consistently outperforms existing methods, particularly in scenarios with extreme data sparsity. Our findings show that accurate, arbitrage-free surface reconstruction is achievable using only 5% of the original data points, with errors 7–12 times lower than competing approaches in high-sparsity scenarios. We rigorously validate the preservation of critical no-arbitrage conditions through probability distribution analysis and total variance strip non-intersection tests. The framework we develop overcomes traditional barriers of limited market data by generating over 13,500 synthetic surfaces for training, compared to typical market availability of fewer than 100. These capabilities have important implications for market risk analysis, derivatives pricing, and the development of more robust risk management frameworks, particularly in emerging markets or for newly introduced derivatives where historical data are scarce. Our integration of machine learning with financial theory constraints represents a significant advancement in volatility surface modeling that balances statistical accuracy with financial relevance. Full article

(This article belongs to the Special Issue Machine Learning Based Risk Management in Finance and Insurance)

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33 pages, 1904 KiB

Open AccessArticle

Interconnectedness of Stock Indices in African Economies Under Financial, Health, and Political Crises

by Anouar Chaouch and Salim Ben Sassi

J. Risk Financial Manag. 2025, 18(5), 238; https://doi.org/10.3390/jrfm18050238 - 30 Apr 2025

Abstract

This study examines the interconnectedness of African stock markets during three major global crises: the 2008 Global Financial Crisis (GFC), the COVID-19 pandemic, and the Russia–Ukraine conflict. We use daily stock index data from 2007 to 2023 for ten African countries and apply a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. The results reveal that volatility connectedness among African markets intensified during all three crises, peaking during the COVID-19 pandemic followed by the 2008 GFC and the Russia–Ukraine conflict. Short-term connectedness consistently exceeded long-term connectedness across all crises. South Africa and Egypt acted as dominant transmitters of volatility, highlighting their systemic importance, while Morocco showed increased influence during the COVID-19 pandemic. These findings suggest that African markets are more globally integrated than previously assumed, making them vulnerable to external shocks. Policy implications include the need for stronger regional financial cooperation, the development of early warning systems, and enhanced intra-African investment to improve market resilience and reduce contagion risk. Full article

(This article belongs to the Special Issue Machine Learning Based Risk Management in Finance and Insurance)

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28 pages, 1606 KiB

Open AccessArticle

Modelling Value-at-Risk and Expected Shortfall for a Small Capital Market: Do Fractionally Integrated Models and Regime Shifts Matter?

by Wafa Souffargi and Adel Boubaker

J. Risk Financial Manag. 2025, 18(4), 203; https://doi.org/10.3390/jrfm18040203 - 9 Apr 2025

Viewed by 314

Abstract

In this study, we examine the relevance of the coexistence of structural change and long memory to model and forecast the volatility of Tunisian stock returns and to deliver a more accurate measure of risk along the lines of VaR and expected shortfall. To this end, we propose three time-series models that incorporate long-term dependence on the level and volatility of returns. In addition, we introduce structural change points using the iterated cumulative sums of squares (ICSS) and the modified ICSS algorithms, synonymous with stock market turbulence, into the conditional variance equations of the models studied. We choose a conditional innovation density function other than the normal distribution, that is, a Student distribution, to account for excess kurtosis. The empirical results show that the inclusion of structural breakpoints in the conditional variance equation and Dual LM provides better short- and long-term predictability. Within such a framework, the ICSS-ARFIMA-HYGARCH model with Student’s t distribution was able to account for the long-term dependence in the level and volatility of TUNINDEX index returns, excess kurtosis, and structural changes, delivering an accurate estimator of VaR and expected shortfall. Full article

(This article belongs to the Special Issue Machine Learning Based Risk Management in Finance and Insurance)

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19 pages, 1488 KiB

Open AccessArticle

Who Is Leading in Communication Tone? Wavelet Analysis of the Fed and the ECB

by Pinar Deniz and Thanasis Stengos

J. Risk Financial Manag. 2025, 18(4), 191; https://doi.org/10.3390/jrfm18040191 - 2 Apr 2025

Viewed by 325

Abstract

This study examines the relationship between the communication tone of the Fed and that of the ECB over the period from January 2000 to September 2023. The tones were measured using both lexicon-based and transform-based algorithms. Wavelet coherence analysis helped distinguish the scale of the relationship over time and frequency domains. Our findings suggest a dynamic process regarding the lead/lag positions, and the similarity of the two algorithms in the medium run highlights the leading role of the ECB during the (pre-)crisis period of the US and the leading role of the Fed during the QE period of the ECB. Hence, the variability in the leader/follower role suggests no strong predictive structural relationship between the two communication tones. Full article

(This article belongs to the Special Issue Machine Learning Based Risk Management in Finance and Insurance)

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18 pages, 1382 KiB

Open AccessArticle

Finite Mixture at Quantiles and Expectiles

by Marilena Furno

J. Risk Financial Manag. 2025, 18(4), 177; https://doi.org/10.3390/jrfm18040177 - 27 Mar 2025

Viewed by 141

Abstract

Finite mixture regression identifies homogeneous groups within a sample and computes the regression coefficients in each group. Groups and group coefficients are jointly estimated using an iterative approach. This work extends the finite mixture estimator to the tails of the distribution, by incorporating quantiles and expectiles and relaxing the constraint of constant group probability adopted in previous analysis. The probability of each group depends on the selected location: an observation can be allocated in the best-performing group if we look at low values of the dependent variable, while at higher values it may be assigned to the poorly performing class. We explore two case studies: school data from a PISA math proficiency test and asset returns from the Center for Research in Security Prices. In these real data examples, group classifications change based on the selected location of the dependent variable, and this has an impact on the regression estimates due to the joint computation of class probabilities and class regressions coefficients. A Monte Carlo experiment is conducted to compare the performances of the discussed estimators with results of previous research. Full article

(This article belongs to the Special Issue Machine Learning Based Risk Management in Finance and Insurance)

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24 pages, 475 KiB

Open AccessArticle

Price Gaps and Volatility: Do Weekend Gaps Tend to Close?

by Marnus Janse van Rensburg and Terence Van Zyl

J. Risk Financial Manag. 2025, 18(3), 132; https://doi.org/10.3390/jrfm18030132 - 3 Mar 2025

Viewed by 957

Abstract

This study investigates weekend price gaps in three major stock market indices—the Dow Jones Industrial Average (DJIA), NASDAQ, and Germany’s DAX—from 2013 to 2023, using high-frequency (5 min) data to explore whether gap movements arise from random volatility or reflect systematic market tendencies. We examine 205 weekend gaps in the DJIA, 270 in NASDAQ, and 406 in the DAX. Two principal hypotheses guide our inquiry as follows: (i) whether price movements into the gap are primarily driven by increased volatility and (ii) whether larger gaps are associated with heightened volatility. Employing Chi-square tests for the independence and linear regression analyses, our results show no strong, universal bias towards closing gaps at shorter distances across all three indices. However, at medium-to-large distances, significant directional patterns emerge, particularly in the DAX. This outcome challenges the assumption that weekend gaps necessarily “fill” soon after they open. Moreover, larger gap sizes correlate with elevated volatility in both the DJIA and NASDAQ, underscoring that gaps can serve as leading indicators of near-term price fluctuations. These findings suggest that gap-based anomalies vary by market structure and geography, raising critical questions about the universality of efficient market principles and offering practical insights for risk management and gap-oriented trading strategies. Full article

(This article belongs to the Special Issue Machine Learning Based Risk Management in Finance and Insurance)

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17 pages, 876 KiB

Open AccessArticle

Machine Learning as a Tool for Assessment and Management of Fraud Risk in Banking Transactions

by Antonio Dichev, Silvia Zarkova and Petko Angelov

J. Risk Financial Manag. 2025, 18(3), 130; https://doi.org/10.3390/jrfm18030130 - 2 Mar 2025

Cited by 1 | Viewed by 1743

Abstract

The present work aims to fill the gaps in existing research on the application of machine learning in fraud detection and management in the banking sector. It provides a theoretical perspective on the evolution of algorithms, highlights practical aspects, and derives relevant metrics for evaluating their performance on unbalanced data. In the growing context of artificial intelligence, the adoption of an innovative, systematic approach to studying fraud in banking transactions through advanced machine learning algorithms is completely positive for the overall accuracy and effectiveness of risk management and has really practical and applied significance. The proven methodology (Classification and Regression Trees, Gradient Boosting, and Extreme Gradient Boosting) was tested on nearly 1.5 million in the banking sector, confirming the observations related to the application of fundamental assessments and specialized statistical methods through machine learning algorithms, demonstrating superior discriminatory power compared to classical models. The development provides valuable insights for managers, researchers, and policymakers aiming to strengthen the security and resilience of banking systems in times of evolving financial fraud challenges. Full article

(This article belongs to the Special Issue Machine Learning Based Risk Management in Finance and Insurance)

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22 pages, 4157 KiB

Open AccessArticle

Prediction of Green Sukuk Investment Interest Drivers in Nigeria Using Machine Learning Models

by Mukail Akinde, Olasunkanmi Olapeju, Olusegun Olaiju, Timothy Ogunseye, Adebayo Emmanuel, Sekinat Olagoke-Salami, Foluke Oduwole, Ibironke Olapeju, Doyinsola Ibikunle and Kehinde Aladelusi

J. Risk Financial Manag. 2025, 18(2), 89; https://doi.org/10.3390/jrfm18020089 - 6 Feb 2025

Viewed by 884

Abstract

This study developed and evaluated machine learning models (MLMs) for predicting the drivers of green sukuk investment interest (GSII) in Nigeria, adopting the planks of hypothesised determinants adapted from variants of the planned behavioural model and behavioural finance theory. Of the seven models leveraged in the prediction, random forest, which had the highest level of accuracy (82.35% for testing and 90.37% for training datasets), with a good R² value (0.774), afforded the optimal choice for prediction. The random forest model ultimately classified 10 of the hypothesised predictors of GSII, which underpinned constructs such as risk, perceived behavioural control, information availability, and growth, as highly important; 21, which were inclusive of all of the hypothesised constructs in measurement, as moderately important; and the remaining 15 as low in importance. The feature importance determined by the random forest model afforded an indicator-specific value, which can help green sukuk (GS) issuers to prioritise the most important drivers of investment interest, suggest important contexts for ethical investment policy enhancement, and inform insights about optimal resource allocation and pragmatic recommendations for stakeholders with respect to the funding of climate change mitigation projects in Nigeria. Full article

(This article belongs to the Special Issue Machine Learning Based Risk Management in Finance and Insurance)

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20 pages, 470 KiB

Open AccessArticle

Modeling and Forecasting the Probability of Crypto-Exchange Closures: A Forecast Combination Approach

by Said Magomedov and Dean Fantazzini

J. Risk Financial Manag. 2025, 18(2), 48; https://doi.org/10.3390/jrfm18020048 - 22 Jan 2025

Viewed by 2421

Abstract

The popularity of cryptocurrency exchanges has surged in recent years, accompanied by the proliferation of new digital platforms and tokens. However, the issue of credit risk and the reliability of crypto exchanges remain critical, highlighting the need for indicators to assess the safety of investing through these platforms. This study examines a unique, hand-collected dataset of 228 cryptocurrency exchanges operating between April 2011 and May 2024. Using various machine learning algorithms, we identify the key factors contributing to exchange shutdowns, with trading volume, exchange lifespan, and cybersecurity scores emerging as the most significant predictors. Since individual machine learning models often capture distinct data characteristics and exhibit varying error patterns, we employ a forecast combination approach by aggregating multiple predictive distributions. Specifically, we evaluate several specifications of the generalized linear pool (GLP), beta-transformed linear pool (BLP), and beta-mixture combination (BMC). Our findings reveal that the beta-transformed linear pool and the beta-mixture combination achieve the best performances, improving forecast accuracy by approximately 4.1% based on a robust H-measure, which effectively addresses the challenges of misclassification in imbalanced datasets. Full article

(This article belongs to the Special Issue Machine Learning Based Risk Management in Finance and Insurance)

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Other

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25 pages, 2263 KiB

Open AccessSystematic Review

Factors, Forecasts, and Simulations of Volatility in the Stock Market Using Machine Learning

by Juan Mansilla-Lopez, David Mauricio and Alejandro Narváez

J. Risk Financial Manag. 2025, 18(5), 227; https://doi.org/10.3390/jrfm18050227 - 24 Apr 2025

Viewed by 502

Abstract

Volatility is a risk indicator for the stock market, and its measurement is important for investors’ decisions; however, few studies have investigated it. Only two systematic reviews focusing on volatility have been identified. In addition, with the advance of artificial intelligence, several machine learning algorithms should be reviewed. This article provides a systematic review of the factors, forecasts and simulations of volatility in the stock market using machine learning (ML) in accordance with PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) review selection guidelines. From the initial 105 articles that were identified from the Scopus and Web of Science databases, 40 articles met the inclusion criteria and, thus, were included in the review. The findings show that publication trends exhibit a growth in interest in stock market volatility; fifteen factors influence volatility in six categories: news, politics, irrationality, health, economics, and war; twenty-seven prediction models based on ML algorithms, many of them hybrid, have been identified, including recurrent neural networks, long short-term memory, support vector machines, support regression machines, and artificial neural networks; and finally, five hybrid simulation models that combine Monte Carlo simulations with other optimization techniques are identified. In conclusion, the review process shows a movement in volatility studies from classic to ML-based simulations owing to the greater precision obtained by hybrid algorithms. Full article

(This article belongs to the Special Issue Machine Learning Based Risk Management in Finance and Insurance)

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34 pages, 4627 KiB

Open AccessSystematic Review

Challenges of Artificial Intelligence for the Prevention and Identification of Bankruptcy Risk in Financial Institutions: A Systematic Review

by Luis-Javier Vásquez-Serpa, Ciro Rodríguez, Jhelly-Reynaluz Pérez-Núñez and Carlos Navarro

J. Risk Financial Manag. 2025, 18(1), 26; https://doi.org/10.3390/jrfm18010026 - 10 Jan 2025

Viewed by 1958

Abstract

The identification and prediction of financial bankruptcy has gained relevance due to its impact on economic and financial stability. This study performs a systematic review of artificial intelligence (AI) models used in bankruptcy prediction, evaluating their performance and relevance using the PRISMA and PICOC frameworks. Traditional models such as random forest, logistic regression, KNN, and neural networks are analyzed, along with advanced techniques such as Extreme Gradient Boosting (XGBoost), convolutional neural networks (CNN), long short-term memory (LSTM), hybrid models, and ensemble methods such as bagging and boosting. The findings highlight that, although traditional models are useful for their simplicity and low computational cost, advanced techniques such as LSTM and XGBoost stand out for their high accuracy, sometimes exceeding 99%. However, these techniques present significant challenges, such as the need for large volumes of data and high computational resources. This paper identifies strengths and limitations of these approaches and analyses their practical implications, highlighting the superiority of AI in terms of accuracy, timeliness, and early detection compared to traditional financial ratios, which remain essential tools. In conclusion, the review proposes approaches that integrate scalability and practicality, offering predictive solutions tailored to real financial contexts with limited resources. Full article

(This article belongs to the Special Issue Machine Learning Based Risk Management in Finance and Insurance)

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