Behavioral Biometrics and Machine Learning for Enhanced Fraud Detection in Financial Services

Abstract

Digital financial services have witnessed exponential growth, enhancing accessibility and convenience for users worldwide. However, this rapid digitalization has also amplified exposure to financial fraud, resulting in substantial economic losses and undermining consumer trust. Traditional fraud detection systems predominantly rely on transactional analysis and rule-based mechanisms, which are limited in detecting adaptive and sophisticated fraudulent activities that imitate legitimate user behavior. Behavioral biometrics, which captures the unique patterns of human-computer interaction including keystroke dynamics, touchscreen gestures, mouse movement trajectories, and device usage patterns provides an innovative layer for identity verification and anomaly detection. When integrated with machine learning models, especially deep learning architectures capable of temporal and sequential modeling, behavioral biometrics enables robust, real-time fraud detection.

This paper presents a comprehensive framework for deploying behavioral biometrics integrated with machine learning to enhance fraud detection in financial services. We explore behavioral data acquisition, preprocessing, feature extraction, modeling strategies, multimodal fusion, evaluation metrics, privacy and ethical considerations, interpretability, deployment challenges, and case studies. Additionally, we examine emerging threats and discuss how adaptive, privacy-preserving machine learning techniques can provide resilient defenses against complex attacks. The paper is intended to inform both research and industry practice, offering a scholarly foundation for high-performing, ethical, and interpretable fraud detection systems.