Hybrid ML and DL Methods for Financial Risk Assessment and Fraud Detection

Abstract

The financial industry is increasingly vulnerable to sophisticated fraud schemes and complex risk environments, necessitating advanced detection and assessment methodologies. This chapter presents a comprehensive exploration of hybrid machine learning (ML) and deep learning (DL) models for financial risk assessment and fraud detection. We introduce a novel hybrid framework that synergizes the strengths of traditional ML algorithms— such as Random Forest, Support Vector Machines, and Gradient Boosting—with advanced DL architectures, including Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks with attention mechanisms. The proposed methodology is designed to address critical challenges in financial data, such as class imbalance, high dimensionality, and evolving fraud patterns. Through a simulated case study on a synthetic credit card transaction dataset, we demonstrate the superior performance of the hybrid approach compared to individual models. The results, visualized through confusion matrices, ROC curves, and precision-recall curves, indicate a significant improvement in detection accuracy, precision, and recall, achieving an F1-score of 94.63provides a detailed discussion of the model’s architecture, implementation, and performance, offering valuable insights for academics and practitioners in the field of financial technology and intelligent systems.