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AI and Automation in Finance: Risk, Regulation, and Strategic Applications
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AI and Automation in Finance: Risk, Regulation, and Strategic Applications

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 December 2026 | Viewed by 7192

Special Issue Editors

Dr. Alessio Faccia

E-Mail Website
Guest Editor
School of Business and Law, University of Birmingham Dubai, Dubai P.O. Box 341799, United Arab Emirates
Interests: finance; accounting; audit; technology
Special Issues, Collections and Topics in MDPI journals
Dr. Pythagoras Petratos

E-Mail Website
Guest Editor
Westminster Business School, University of Westminster, London NW1 5LS, UK
Interests: fintech; finance; technology

Special Issue Information

Dear Colleagues,

The financial sector is experiencing a fundamental transformation through the deployment of artificial intelligence (AI) and automation technologies. From predictive analytics and algorithmic trading to risk modelling, regulatory technology, and compliance monitoring, AI applications are redefining financial decision making, market efficiency, and systemic risk. Automation reduces operational costs and enhances accuracy, but it also introduces challenges related to model transparency, governance, accountability, and cybersecurity.

This Special Issue seeks original research, reviews, and case studies that address the opportunities and risks of AI and automation in the finance sector. Contributions are welcome from both academic researchers and practitioners exploring the integration of AI into financial services, its implications for financial risk management, and the regulatory and ethical frameworks needed to ensure safe adoption.

Papers may address themes including machine learning models for credit and market risk assessment, robotic process automation in auditing, algorithmic trading risks, stress-testing of AI-based systems, explainable AI in financial decision making, regulatory compliance through RegTech, fraud detection, and the socio-economic impact of AI adoption. Empirical, theoretical, and methodological studies are encouraged.

Dr. Alessio Faccia
Dr. Pythagoras Petratos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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 1600 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

  • artificial intelligence in finance
  • automation and financial risk
  • algorithmic trading
  • regulatory technology (RegTech)
  • model risk and explainability
  • financial fraud detection
  • ai governance in financial services
  • machine learning for risk modelling
  • FinTech and digital transformation.

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Published Papers (3 papers)

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Research

15 pages, 1190 KB  
Article
Explainable AI (XAI) in Auditing: Bridging the Gap Between Predictive Fraud Models and Regulatory Standards
by Alessio Faccia
J. Risk Financial Manag. 2026, 19(5), 311; https://doi.org/10.3390/jrfm19050311 - 25 Apr 2026
Viewed by 1277
Abstract
This article examines whether a high-performing fraud detection model can also meet the demands of auditability, documentation, and regulatory transparency. Using the publicly available European credit card fraud dataset of 284,807 transactions, including 492 fraudulent cases, this study compares weighted logistic regression with [...] Read more.
This article examines whether a high-performing fraud detection model can also meet the demands of auditability, documentation, and regulatory transparency. Using the publicly available European credit card fraud dataset of 284,807 transactions, including 492 fraudulent cases, this study compares weighted logistic regression with XGBoost under severe class imbalance. Model performance is assessed through precision, recall, F1 score, ROC AUC, and precision–recall AUC, with particular attention to alert burden and fraud capture. Results show that XGBoost materially outperforms logistic regression in operational terms. While logistic regression achieves slightly higher recall, XGBoost raises precision from 0.061 to 0.562, improves PR AUC from 0.719 to 0.863, and reduces false positives from 1386 to 67. The PR AUC of 0.863 refers to the cross-validated average reported in the model comparison, while the holdout test result reported later in this paper is 0.852. It cuts the review queue from 1476 alerts to 153 while still identifying 86 of 98 fraud cases in the test set. Explainability is then introduced through SHAP, which provides both global feature attribution and transaction-level reasoning. The findings show that SHAP makes the boosted model readable at the level of both overall model behaviour and individual fraud flags, thereby supporting audit review, model validation, and regulatory scrutiny. The article argues that the combination of XGBoost and SHAP offers a stronger fit for auditing than either a weaker but transparent linear model or a stronger opaque classifier. One limit remains, since the dataset contains anonymised principal components rather than original business variables, which restricts semantic interpretation. Even so, the workflow provides a practical bridge between predictive fraud analytics and the demands of explainable, reviewable, and accountable AI in auditing. Full article
(This article belongs to the Special Issue AI and Automation in Finance: Risk, Regulation, and Strategic Applications)
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18 pages, 754 KB  
Article
AI and Fintech Synergy: Strengthening Financial Stability in Islamic and Conventional Banks
by Fahad Abdulrahman Alahmad, Ghulam Ghouse and Muhammad Ishaq Bhatti
J. Risk Financial Manag. 2026, 19(1), 21; https://doi.org/10.3390/jrfm19010021 - 1 Jan 2026
Viewed by 2123
Abstract
Artificial intelligence (AI) has played a pivotal role in enhancing the efficiency of financial technology (Fintech), ultimately contributing to the stability of the banking sector. The advancements in Fintech driven by AI tools are significantly improving risk management within the banking industry. This [...] Read more.
Artificial intelligence (AI) has played a pivotal role in enhancing the efficiency of financial technology (Fintech), ultimately contributing to the stability of the banking sector. The advancements in Fintech driven by AI tools are significantly improving risk management within the banking industry. This paper investigates the mediating role of AI in the relationship between Fintech and financial stability in the context of Islamic and conventional banks across selected countries in the Organization of Islamic Cooperation (OIC). It employs structural equation modeling (SEM) to explore the causal linkages across time domains. The results of this research identify that AI is a significant mediator, playing a critical role between Fintech and stability. It either mitigates or amplifies risks, depending on the regulatory framework and implementation practices in place. The analysis indicates that AI has a weak mediating effect in the short run, but a strong mediating effect in the long run between Fintech and stability. This research paper emphasizes the importance of developing robust, forward-thinking policies to leverage the benefits of AI. It also addresses the risks to financial stability in both Islamic and conventional banking systems. Full article
(This article belongs to the Special Issue AI and Automation in Finance: Risk, Regulation, and Strategic Applications)
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32 pages, 1696 KB  
Article
Financial Statement Fraud Detection Through an Integrated Machine Learning and Explainable AI Framework
by Tsolmon Sodnomdavaa and Gunjargal Lkhagvadorj
J. Risk Financial Manag. 2026, 19(1), 13; https://doi.org/10.3390/jrfm19010013 - 24 Dec 2025
Cited by 3 | Viewed by 3260
Abstract
Financial statement fraud remains a substantial risk in environments marked by weak regulatory oversight and information asymmetry. This study develops a decision-centric framework that integrates machine learning, explainable artificial intelligence, and decision curve analysis to improve fraud detection under severe class imbalance. Using [...] Read more.
Financial statement fraud remains a substantial risk in environments marked by weak regulatory oversight and information asymmetry. This study develops a decision-centric framework that integrates machine learning, explainable artificial intelligence, and decision curve analysis to improve fraud detection under severe class imbalance. Using 969 firm-year observations from 132 Mongolian firms (2013–2024), we evaluate 21 financial ratios with models including Random Forest, XGBoost, LightGBM, MLP, TabNet, and a Stacking Ensemble trained with SMOTE and class-weighted learning. Performance was assessed using PR-AUC, F1-score, Recall, and DeLong-based significance testing. The Stacking Ensemble achieved the strongest results (PR-AUC = 0.93; F1 = 0.83), outperforming both classical and modern baseline models. Interpretability analyses (SHAP, LIME, and counterfactual explanations) consistently identified leverage, profitability, and liquidity indicators as dominant drivers of fraud risk, supported by a SHAP Stability Index of 0.87. Decision curve analysis showed that calibrated thresholds improved decision efficiency by 7–9% and reduced over-audit costs by 3–4%, while an audit cost simulation estimated annual savings of 80–100 million MNT. Overall, the proposed ML–XAI–DCA framework offers a transparent, interpretable, and cost-efficient approach for enhancing fraud detection in emerging-market contexts with limited textual disclosures. Full article
(This article belongs to the Special Issue AI and Automation in Finance: Risk, Regulation, and Strategic Applications)
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