Predictive Machine Learning Models for Detecting Financial Fraud and Credit Risk Across Digital Banking Platforms

Author

Amila Chaminda


Abstract

 The rapid expansion of digital banking has transformed financial service delivery through mobile applications, online banking, instant payments, digital lending, and API-driven financial ecosystems. However, increased transaction volumes, interconnected platforms, and evolving customer behaviours have intensified exposure to financial fraud and credit risk, challenging conventional rule-based monitoring and static credit-scoring approaches. This study presents a predictive machine learning framework for detecting fraudulent activities and assessing credit risk across digital banking platforms. The framework integrates transactional, behavioural, demographic, account, device, and credit-history data to construct risk-sensitive features capable of identifying complex patterns associated with anomalous transactions and potential borrower default. Supervised and ensemble learning models are applied to fraud classification and credit-risk prediction, while class-imbalance handling, feature selection, hyperparameter optimisation, and cross-validation strengthen predictive robustness. Model effectiveness is evaluated using precision, recall, F1-score, ROC-AUC, false-positive rate, and predictive accuracy. By combining fraud detection and credit-risk analytics within an integrated predictive architecture, the proposed approach supports earlier risk identification, adaptive decision-making, reduced financial losses, and more resilient digital banking operations across increasingly complex financial environments.


Keywords

Digital Banking; Financial Fraud Detection; Credit Risk Prediction; Machine Learning; Predictive Analytics; Financial Risk Management



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References


  1. Dwadasi A. Artificial intelligence and machine learning in financial services: risk management and fraud detection. J. Electrical Systems. 2024;20(6s):1418-24.
  2. Samson Onaopemipo Amoran, Okolie Awele, Oluwatosin Lawal, Okon Godspower Emmanuel. (2026). An Explainable Artificial Intelligence Framework for Early Warning of Systemic Financial Risk. Journal of Computational Analysis and Applications (JoCAAA), 35(4), 69–81. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5307
  3. Shearing C. Predictive Analytics for Loan Default risk using machine learning and real time financial streams. ThinkTide Global Research Journal. 2023 Dec 5;4(4):15-29.
  4. Chukwunweike J. Coordinating PLC-based load shedding and variable-speed drives to prevent transformer overloading during industrial production peaks. Int J Electr Data Commun. 2025;6(2 Pt B):118-135. doi:10.22271/27083969.2025.v6.i2b.109.
  5. Afriyie JK, Tawiah K, Pels WA, Addai-Henne S, Dwamena HA, Owiredu EO, Ayeh SA, Eshun J. A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions. Decision Analytics Journal. 2023 Mar 1;6:100163.
  6. Khatib S. The application of machine learning models in fraud detection and prevention across digital banking channels and payment platforms. International Journal of Advanced Computational Methodologies and Emerging Technologies. 2024 Sep 4;14(9):1-7.
  7. Obunadike TC. Leveraging AI forecasting to quantify tariff-induced food price volatility in net-importing nations. Int J Res Publ Rev. 2025;6(6):12441-12458. doi:10.55248/gengpi.6.0625.23102.
  8. Javaid HA. Improving fraud detection and risk assessment in financial service using predictive analytics and data mining. Integrated Journal of Science and Technology. 2024 Aug 28;1(3).
  9. Samuel Addo. Harnessing AI-powered financial forecasting tools to improve risk management, investment strategies, and overall corporate profitability across industries. International Journal of Science and Research Archive, 2023, 09(02), 1175-1192. Article DOI: https://doi.org/10.30574/ijsra.2023.9.2.0653
  10. Jakir T, Rabbi MN, Rabbi MM, Ahad MA, Siam MA, Hossain MN, Hasan MS, Hossain A. Machine Learning-Powered Financial Fraud Detection: Building Robust Predictive Models for Transactional Security. Journal of Economics, Finance and Accounting Studies. 2023 Oct 19;5(5):161-80.
  11. Sekinobe M, Mukasa K, Nayebale FI, Kato J. An interdisciplinary framework for the development of intelligent accounting, automation systems integrating: predictive risk analytics and dynamic internal control mechanisms to enhance regulatory compliance and fraud mitigation in high-risk economic sectors. Int J Innov Sci Res Technol. 2026 Jan. doi:10.38124/ijisrt/25dec1138.
  12. Thar KW, Wai TT. Machine learning based predictive modelling for fraud detection in digital banking. In2024 IEEE Conference on Computer Applications (ICCA) 2024 Mar 16 (pp. 1-5). IEEE.
  13. Uzoh E. Explainable AI and customer behavioral signals for high-precision account takeover and identity risk detection in e-commerce platforms. Asian J Manage Commerce. 2025;6(2 Pt V):2079-2090. doi:10.22271/27084515.2025.v6.i2v.1443
  14. Konatham MR, Uddandarao DP, Vadlamani RK, Nair R. Optimizing Credit Risk Assessment in Digital Banking Through AI-Driven Predictive Science. In2025 IEEE International Conference on Advanced Computing Technologies (ICACT) 2025 Sep 1 (pp. 478-483). IEEE.
  15. Ogan RK. Predictive anomaly detection for real-time virtual account transactions and API-driven payment processing across enterprise banking platforms. Int J Comput Appl Technol Res. 2024;13(12):298-308. doi:10.7753/IJCATR1312.1023.
  16. Almazroi AA, Ayub N. Online payment fraud detection model using machine learning techniques. Ieee Access. 2023 Dec 4;11:137188-203.
  17. AbouGrad H, Sankuru L. Online banking fraud detection model: Decentralized machine learning framework to enhance effectiveness and compliance with data privacy regulations. Mathematics. 2025 Jun 27;13(13):2110.
  18. Pamisetty A, Paleti S, Adusupalli B, Singireddy J, Inala R, Nagabhyru KC. Explainable AI Systems for Credit Scoring and Loan Risk Assessment in Digital Banking Platforms. In2025 IEEE 13th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) 2025 Sep 4 (pp. 1478-1483). IEEE.
  19. K. Pilla, "Sweat2Scroll: An Energy-Efficient Policy Enforcement Architecture for Mobile Edge Environments Using WebAssembly," SoutheastCon 2026, Huntsville, AL, USA, 2026, pp. 01-08, doi: 10.1109/SoutheastCon63549.2026.11476032.
  20. Sultan M. Machine learning models for financial risk assessment. Iconic Research and Engineering Journals. 2025;8(10):330-8.
  21. Alawode A, Obunadike TC. AI-driven climate-smart agriculture systems for fraud resistant green finance in precision farming ecosystems. Int J Comput Appl Technol Res. 2023;12(12):168-184. doi:10.7753/IJCATR1212.1019.
  22. Olowe KJ, Edoh NL, Zouo SJ, Olamijuwon J. Review of predictive modeling and machine learning applications in financial service analysis. Computer Science & IT Research Journal. 2024;5(11):2609-26.
  23. Ogan RK. Machine-learning framework for predicting point-of-sale integration failures across digital ordering, payment, and third-party delivery technology ecosystems. Int J Res Publ Rev. 2025;6(12):8887-8898.
  24. Ononiwu M, Azonuche TI, Okoh OF, Enyejo JO. Machine learning approaches for fraud detection and risk assessment in mobile banking applications and fintech solutions. International Journal of Scientific Research in Science, Engineering and Technology. 2023 Jul;10(4):1-0.
  25. Nayebale FI, Kato J, Wycliff N, Kyakuwaire A. A blockchain-integrated tax access layer for secure data protection, standardized service delivery, and improved equitable access in support of national fiscal modernization. Int J Multidiscip Res. 2026;8(1). doi:10.36948/ijfmr.2026.v08i01.66225.
  26. Aggarwal L, Saravanan T, Loana A. Digital Banking and the Future of Embedded Finance: How Will AI-Powered Fraud Detection and Algorithmic Credit Scoring in Cross-Border Rails Reshape Systemic Risk?. Yes the paper has also been accepted in the Ox Journal. 2025 Nov 29.
  27. Obunadike TGC, Alawode A. AI-enhanced scenario planning for U.S. food trade policy: anticipating global supply chain shocks and food insecurity risks. World J Adv Res Rev. 2025;26(2):943-962. doi:10.30574/wjarr.2025.26.2.1786.
  28. Dichev A, Zarkova S, Angelov P. Machine learning as a tool for assessment and management of fraud risk in banking transactions. Journal of Risk and Financial Management. 2025 Mar 2;18(3):130.
  29. Racheal Kikachukwu Ogan. Machine-learning-based API failure prediction and root-cause analytics for improving reliability of enterprise banking integration architectures at scale. Int J Comput Artif Intell 2021;2(2):142-153. DOI: 33545/27076571.2021.v2.i2a.378
  30. Ahmmed MJ. Systematic review and quantitative evaluation of advanced machine learning frameworks for credit risk assessment, fraud detection, and dynamic pricing in US financial systems. International Journal of Business and Economics Insights. 2025 Dec 27;5(3):1329-69.
  31. Obunadike TC. Leveraging AI forecasting to quantify tariff-induced food price volatility in net-importing nations. Int J Res Publ Rev. 2025;6(6):12441-12458. doi:10.55248/gengpi.6.0625.23102.
  32. Fonkem BN. AI-powered risk scoring models for real-time fraud detection in digital banking ecosystems. Journal of Computational Analysis and Applications. 2025 Nov;34(11):349-71.
  33. Nweke OP. Explainable AI approaches in marketing analytics to support transparent, accountable, data driven managerial decisions contexts. Int J Comput Artif Intell. 2023;4(1):89-102. doi:10.33545/27076571.2023.v4.i1a.269
  34. George MZ, Alam MK, Hasan MT. Machine learning for fraud detection in digital banking: a systematic literature review REVIEW. arXiv preprint arXiv:2510.05167. 2025 Oct 4.
  35. Ogan RK. Risk-based API governance for predicting integration failures, security anomalies, and service degradation across hybrid enterprise application environments. Int J Finance Manage Econ. 2024;7(2):871-882. doi:10.33545/26179210.2024.v7.i2.946.
  36. Tian X, Tian Z, Khatib SF, Wang Y. Machine learning in internet financial risk management: A systematic literature review. Plos one. 2024 Apr 16;19(4):e0300195.
  37. Okonoda ME. Applying blockchain-based fraud prevention mechanisms for securing digital banking transactions and strengthening customer trust in financial institutions worldwide. Int J Adv Res Publ Rev. 2025;2(9):351-367. doi:10.55248/gengpi.6.0925.3334.
  38. Pasumarthi H. Applying machine learning to high-volume banking platforms: From transaction data to predictive risk intelligence. International Journal of Computer Technology and Electronics Communication. 2023 Aug 21;6(4):7352-6.
  39. Obunadike TC, Ossi BA. Explainable AI for early detection of food-system-driven micronutrient deficiencies in rural populations. Int J Res Publ Rev. 2026;7(6):7130-7144. doi:10.55248/gengpi.07.0626.18b12.
  40. Pamisetty A. AI Powered Predictive Analytics in Digital Banking and Finance: A Deep Dive into Risk Detection, Fraud Prevention, and Customer Experience Management. Fraud Prevention, and Customer Experience Management (December 11, 2023). 2023 Dec.

 

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