Machine Learning-Based Approaches to Detecting and Preventing Fraud in Digital Financial Channels and Payment Platforms

This paper provides a comprehensive investigation into the application of machine learning models for fraud detection and prevention in digital banking channels and payment systems. It explores the distinct characteristics of the data streams associated with online banking transactions, mobile banking transactions, peer-to-peer transactions, and new payment systems such as digital wallet systems and real-time payment systems. It also provides a detailed discussion of one of the sophisticated mathematical models that formulates the fraud detection model by considering it a stochastic optimization problem under adversarial perturbations. It also uses concepts from measure-theoretic probability theory, reproducing kernel Hilbert space theory, and robust statistical decision theory. It also provides a detailed discussion of how incremental learning can be incorporated into the model to handle concept drifts and meta-learning strategies to leverage knowledge from heterogeneous channels. It also provides a detailed discussion of how the framework can be experimentally evaluated on large-scale synthetic production data sets, demonstrating that ensemble models of deep learning-based representation learning and probabilistic graphical models can achieve significant performance enhancements in detection latency and false-positive rates while maintaining customer experience by adjusting risk scoring thresholds. It concludes with a number of recommendations for deploying the framework and future areas of investigation to achieve fully autonomous fraud resilience.