AI-Driven Financial Auditing in Modern Banking: An Advanced Data-Driven Machine Learning Framework for Predictive Financial Intelligence, Real-Time Fraud Detection, and Automated Risk Monitoring

Abstract

The rapid expansion of digital banking, real-time payment systems, and data-intensive financial services has increased the complexity of financial auditing and created new challenges related to fraud detection, financial-risk assessment, and continuous transaction monitoring. This study proposes an AI-driven financial auditing framework for modern banking that integrates predictive financial intelligence, real-time fraud detection, and automated risk monitoring within a unified data-driven machine learning architecture. A structured banking dataset containing 185,000 financial transactions from 12,500 anonymized customer accounts was developed from transactional, behavioural, account, authentication, credit, and risk-monitoring records collected over a three-year observation period. The dataset included 42 predictive variables, including transaction amount, transaction velocity, account balance variation, payment frequency, beneficiary novelty, failed-login intensity, device inconsistency, unusual access location, credit exposure, cash-flow volatility, historical fraud frequency, repayment behaviour, and audit-risk indicators. Following data cleaning, missing-value treatment, normalization, categorical encoding, class balancing, and feature selection, the dataset was divided into 70% training, 15% validation, and 15% testing subsets. Logistic Regression, Support Vector Machine, Random Forest, Artificial Neural Network, and XGBoost models were comparatively evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and false-alert rate. Experimental results demonstrated that XGBoost achieved the strongest overall performance, obtaining 97.2% accuracy, 96.8% precision, 96.5% recall, 96.6% F1-score, and a ROC-AUC of 0.989, compared with 86.4% accuracy for Logistic Regression, 90.7% for SVM, 94.3% for Random Forest, and 95.8% for the neural network. The proposed auditing framework reduced false-positive audit alerts by 31.6%, improved suspicious-transaction detection by 24.8%, decreased average risk-identification time by 38.7%, and increased automated high-risk account recognition from 81.5% to 96.9%. Predictive financial analytics further achieved 94.6% accuracy in early financial-risk classification, enabling proactive identification of abnormal liquidity patterns, credit deterioration, and emerging transaction anomalies. Overall, the proposed framework demonstrates that machine learning can substantially improve audit accuracy, operational efficiency, fraud intelligence, and continuous financial-risk surveillance, providing a scalable foundation for intelligent and automated auditing in modern banking environments.

References

Adewale, T. T., Olorunyomi, T. D., & Odonkor, T. N. (2023). Big data-driven financial analysis: A new paradigm for strategic insights and decision-making. Journal of Financial Innovation and Analytics, 1(1), 1–15.

Ahirrao, Y. S., Ansari, I., Azim, K. S., Bhujel, K., & Panchal, S. S. (2025). AI-powered financial strategy: Transforming business decision-making through predictive analytics. The USA Journals TAJET, 7(9), 126–151.

Ahmad, B., Ali, S., Muneer, M., Fatima, A., & Abbas, N. (2025). Wind energy integration into the SAARC region: A comprehensive review of optimal wind sites for a sustainable super smart grid. Wind Energy, 3(2).

Ahmad, B., Jillani, S. A., Rafique, M., & Soomro, A. A. (2026, January). Design and monitoring of a tree-inspired vertical axis wind turbine for efficient urban wind utilization. In 2026 1st International Conference on Innovations in Information and Communication Technologies (IICT) (pp. 1–6). IEEE.

Ahmad, B., Majeed, M. K., Soomro, A. A., & Jillani, S. A. (2026, January). Enhancing short-term voltage stability in wind-assisted microgrids using STATCOM technology. In 2026 1st International Conference on Innovations in Information and Communication Technologies (IICT) (pp. 1–6). IEEE.

Aljunaid, S. K., Almheiri, S. J., Dawood, H., & Khan, M. A. (2025). Secure and transparent banking: Explainable AI-driven federated learning model for financial fraud detection. Journal of Risk and Financial Management, 18(4), 179.

Ashraf, S., & Khalid, N. (2024). Transforming financial services: AI-driven predictive analytics for smart operations and risk assessment.

Bandi, V. D. V. K. (2024). AI-driven predictive risk modeling architectures for financial systems. International Journal of Finance, 37(3), 54–78.

Beauty, A. M. (2025). Explainable AI in data-driven finance: Balancing algorithmic transparency with operational optimization demands. International Journal of Advanced Research Publication and Reviews, 2(6), 125–149.

Celestin, M., & Mishra, A. K. (2025). AI-driven financial analytics: Enhancing forecast accuracy, risk management, and decision-making in corporate finance. Janajyoti Journal, 3(1), 1–27.

Daryan, A., Kamyabi, N., Mehraban, S., & Khosravi, T. (2026). AI-driven fraud detection in financial statements: A comparative study of machine learning models. International Journal of Business Management and Entrepreneurship, 5(1), 24–40.

Dorsey, J. (2025). AI-powered predictive analytics for risk management and fraud detection in modern banking (SSRN No. 7043898). SSRN.

Elumilade, O. O., Ogundeji, I. A., Achumie, G. O., Omokhoa, H. E., & Omowole, B. M. (2021). Enhancing fraud detection and forensic auditing through data-driven techniques for financial integrity and security. Journal of Advanced Education and Sciences, 1(2), 55–63.

Garud, S. (2025). AI-driven risk management in financial services from theory to practice in smart education. In Smart education and sustainable learning environments in smart cities (pp. 77–92). IGI Global Scientific Publishing.

Green, A. (2025). AI-driven financial intelligence systems: A new era of risk detection and strategic analysis. Center for Open Science.

Ismaeil, M. K. A. (2024). Harnessing AI for next-generation financial fraud detection: A data-driven revolution. Journal of Ecohumanism, 3(7), 811–821.

Jacob, I., Oyoh, L., & Joy, D. (n.d.). Cloud-based architectures for AI-driven financial data management in modern banks.

Johri, A., Sayal, A., Chong, K. M., Khoja, M., Jha, J., & Tyagi, N. (2026). Enhancing audit quality and reducing costs: The impact of AI in banking and financial services. Frontiers in Artificial Intelligence, 8, 1718854.

Jun, C., & Paulson, N. (2025). Artificial intelligence and data-driven banking transformation: A review of efficiency and innovations (SSRN No. 5413924). SSRN.

Kalaiselvi, R. (2026). Explainable AI models for cloud-based fraud detection risk assessment and secure financial decision intelligence. International Journal of Technology, Management and Humanities, 12(2), 35–46.

Kumar, S. A. (2026). Artificial intelligence-driven banking and enterprise innovation through secure computing and data-centric systems. International Journal of Science, Research and Technology, 9(3), 844–854.

Machireddy, J. R., Rachakatla, S. K., & Ravichandran, P. (2021). AI-driven business analytics for financial forecasting: Integrating data warehousing with predictive models. Journal of Machine Learning in Pharmaceutical Research, 1(2), 1–24.

Malik, M. (2025). Technological and data-driven management: Artificial intelligence in complex decision-making. In Navigation complexity: Multidisciplinary approaches to management.

Mehmood, F. (2024). AI-driven decision support systems in financial risk management: A comparative study. Multidisciplinary Research in Computing Information Systems, 4(2), 98–109.

Milton, T. (2025). Artificial intelligence in economic and financial decision-making: A comprehensive review. Stout in Economics, Finance and Accounting, 1(1), 1–17.

Nuritdinovich, M. A., Bokhodirovna, K. M., Kavitha, V. O., & Ugli, S. A. O. (2025, June). Advanced AI algorithms in accounting: Redefining accuracy and speed in financial auditing. In AIP Conference Proceedings (Vol. 3306, No. 1, Article 050008). AIP Publishing.

Oko-Odion, C. (2025). AI-driven risk assessment models for financial markets: Enhancing predictive accuracy and fraud detection. International Journal of Computer Applications Technology and Research, 14(4), 80–96.

Olowe, K. J., Edoh, N. L., Zouo, S. J. C., & Olamijuwon, J. (2024). Review of predictive modeling and machine learning applications in financial service analysis. Computer Science & IT Research Journal, 5(11), 2609–2626.

Paleti, S. (2022). Fusion bank: Integrating AI-driven financial innovations with risk-aware data engineering in modern banking. Decision Making, 2326, 9865.

Paleti, S. (2024). Transforming financial risk management with AI and data engineering in the modern banking sector. American Journal of Analytics and Artificial Intelligence.

Peace, N., Luke, I., & Smith, R. (n.d.). AI-powered financial forecasting and predictive analytics in digital banking environments.

Pillai, V. (2023). Integrating AI-driven techniques in big data analytics: Enhancing decision-making in financial markets. International Journal of Engineering and Computer Science, 12(7), 10–18535.

Purwar, M., Deka, U., & Raj, H. (2024, November). Data-driven insights: Leveraging analytics for predictive modeling in finance. In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS) (pp. 687–693). IEEE.

Sundar, J. S., Chandra, S., Saini, R. K., Jha, S., & Katta, S. K. (2025, July). AI-powered predictive analytics for financial risk management in banking and fintech. In 2025 IEEE 4th World Conference on Applied Intelligence and Computing (AIC) (pp. 174–178). IEEE.

Yanney, A. A. S. (2025). Redefining corporate financial governance through AI-powered predictive models for global business risk management. International Journal of Research Publication and Reviews, 2(6), 25–49.