Governed Generative AI for Fraud and Wealth Risk
Keywords:
Generative Financial Intelligence; Fraud Hotspot Signal Processing; Generative Shape-Encoded Adversarial Networks; Generative Neural Coalition; A-TEAM; Real-Time Fraud Detection and Mitigation; Adaptive Portfolio Management; AI Governance for Finance; Wealth Risk Decisioning; Wealth Risk Management; Trustworthy Fraud Governance; Financial Risk Control; Geneva Finance; Global Governance; Risk Materialization Process; Dynamic Risk Governance; Generative Probabilistic Financial Models; Parametric Probabilistic Models; Generative Knowledge; Generative Models; Generative AI; Discriminative Models; Probabilistic Risk Models; Adaptive Wealth Risk Governance; Research-Policy Gap; Data Accessibility.Abstract
Generative Financial Intelligence (GFI) is a formal framework that combines generative modelling with adaptive governance principles to address two core financial challenges: fraud detection and mitigation, and portfolio risk decisioning. By applying this dual focus, a GFI-enabled architecture supports trustworthy decision-making and satisfies the governance quality criteria established by the financial industry's fraud risk-management standards.
Underlying this framework is a general adaptive governance model designed for major data- and model-driven applications spanning fraud detection, fraud mitigation, and wealth management—one that informs a portfolio's long-term strategy. The model employs autonomous systems to anticipate likely outcomes, while explicitly acknowledging the uncertainty inherent in forecasting rare, high-impact events. Economic-inequality-driven systemic collapse, intensified by the 2020 pandemic, is one such event that leaves managed portfolios more vulnerable to external shocks. To address this, GFI validates model behaviour and feedback against closed-loop economic-data dynamics, generating rich scenario analyses for Adaptive Wealth and Portfolio Risk Management (AWPRM) and enabling real-time fraud mitigation.
GFI advances trustworthy AI in finance along two dimensions. First, it situates existing research on data-driven fraud detection and mitigation within a formal governance model for fraud management, enabling quality-assurance evaluation consistent with the organisational-design principles of the Fraud Risk Management Framework. Second, by leveraging generative models, GFI enables principled uncertainty quantification and decision-making that strengthen portfolio risk governance.
References
1. Ashtiani, M. N., & Raahemi, B. (2022). Intelligent fraud detection in financial statements using machine learning and data mining: A systematic literature review. IEEE Access, 10, 72504–72525.
2. Stojanović, B., Božić, J., Hofer-Schmitz, K., Nahrgang, K., Weber, A., Badii, A., Sundaram, M., Jordan, E., & Runevic, J. (2021). Follow the trail: Machine learning for fraud detection in FinTech applications. Sensors, 21(5), 1594.
3. Zhu, X., Ao, X., Qin, Z., & others. (2021). Intelligent financial fraud detection practices in post-pandemic era. The Innovation, 2(4), 100176.
4. Pranitha Potturi. (2023). Cloud-Driven Business Intelligence Modernization for Scalable Enterprise Data Integration and Decision Support. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 387 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8505
5. Canhoto, A. I. (2021). Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective. Journal of Business Research, 131, 441–452.
6. Korol, V., & others. (2021). Usage of machine learning methods for early detection of money laundering schemes. Procedia Computer Science, 190, 184–192.
7. Hadji Misheva, B., Jaggi, D., Posth, J.-A., Gramespacher, T., & Osterrieder, J. (2021). Audience-dependent explanations for AI-based risk management tools: A survey. Frontiers in Artificial Intelligence, 4, 794996.
8. Sasidhar Reddy Mondeddula. (2023). Natural Language Processing for Intelligent Automation of Financial Documents and Banking Operations. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 605–612. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8527
9. Giudici, P., & Raffinetti, E. (2022). Explainable AI methods in cyber risk management. Quality and Reliability Engineering International, 38(3), 1318–1326.
10. Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57, 203–216.
11. Fritz-Morgenthal, S., Hein, B., & Papenbrock, J. (2022). Financial risk management and explainable, trustworthy, responsible AI. Frontiers in Artificial Intelligence, 5, 779799.
12. Zhou, J., Chen, C., Li, L., Zhang, Z., & Zheng, X. (2022). FinBrain 2.0: When finance meets trustworthy AI. Frontiers of Information Technology & Electronic Engineering, 23(12), 1747–1764.
13. Mattaparthi, R. (2023). Deep Learning-Driven Combustion Anomaly Detection in Diesel Powertrains: A Multi-Sensor Fusion Approach for Real-Time ECM Adaptation. International Journal of Intelligent Systems and Applications in Engineering, 11, 1084.
14. Kumar, S., Ahmed, R., Bharany, S., Shuaib, M., Ahmad, T., Tag Eldin, E., Ur Rehman, A., & Shafiq, M. (2022). Exploitation of machine learning algorithms for detecting financial crimes based on customers’ behavior. Sustainability, 14(21), 13875.
15. Tuffveson Jensen, R. I., & Iosifidis, A. (2023). Qualifying and raising anti-money laundering alarms with deep learning. Expert Systems with Applications, 214, 119037.
16. Alexandre, C. R., & Balsa, J. (2023). Incorporating machine learning and a risk-based strategy in an anti-money laundering multiagent system. Expert Systems with Applications, 217, 119500.
17. Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248-260.
18. Zhou, Y., Li, H., Xiao, Z., & Qiu, J. (2023). A user-centered explainable artificial intelligence approach for financial fraud detection. Finance Research Letters, 58, 104309.
19. Zhang, T., Zhu, W., Wu, Y., Wu, Z., Zhang, C., & Hu, X. (2023). An explainable financial risk early warning model based on the DS-XGBoost model. Finance Research Letters, 56, 104045.
20. Khan Achakzai, M. A., & Peng, J. (2023). Detecting financial statement fraud using dynamic ensemble machine learning. International Review of Financial Analysis, 89, 102827.
21. Aftabi, S. Z., Ahmadi, A., & Farzi, S. (2023). Fraud detection in financial statements using data mining and GAN models. Expert Systems with Applications, 227, 120144.
22. Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295-312.
23. Bhowmik, A., Sannigrahi, M., Chowdhury, D., & Dwivedi, A. D. (2022). DBNex: Deep belief network and explainable AI based financial fraud detection. In 2022 IEEE International Conference on Big Data (Big Data). IEEE.
24. Alonso Robisco, A., & Carbó Martínez, J. M. (2022). Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction. Financial Innovation, 8, 70.
25. Cummins, M., Gogolin, F., Kearney, F., & Kiely, G. (2022). Practice-relevant model validation: Distributional parameter risk analysis in financial model risk management. Annals of Operations Research, 318, 107–131.
26. Gourieroux, C., & Monfort, A. (2021). Model risk management: Valuation and governance of pseudo-models. Econometrics and Statistics, 17, 1–22.
27. Aslam, F., Hunjra, A. I., Ftiti, Z., Louhichi, W., & Shams, T. (2022). Insurance fraud detection: Evidence from artificial intelligence and machine learning. Research in International Business and Finance, 62, 101744.
28. Bharath Penaka. (2023). AI-Driven Predictive Analytics for Early Detection of High-Risk Patients in Cloud-Based Healthcare Systems . International Journal of Medical Toxicology and Legal Medicine, 26(3 and 4), 118–124. Retrieved from https://ijmtlm.org/index.php/journal/article/view/1506
29. Shabsigh, G., & Boukherouaa, E. B. (2023). Generative artificial intelligence in finance: Risk considerations. IMF FinTech Note 2023/006. International Monetary Fund.
30. Chen, H. (2023). Large language models in finance: A survey. In Proceedings of the Fourth ACM International Conference on AI in Finance (pp. 374–382). Association for Computing Machinery.
31. Eddin, A. N., Bono, J., Aparício, D., Polido, D., Ascensão, J. T., Bizarro, P., & Ribeiro, P. (2021). Anti-money laundering alert optimization using machine learning with graphs. arXiv.
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