Adversarial Robustness in ML Models for Detecting Synthetic Identity Fraud in Credit Risk

Abstract

Synthetic identity fraud represents a growing threat in credit risk management, where attackers create fictitious identities by combining real and fabricated information to bypass traditional detection systems. Machine learning models have demonstrated effectiveness in detecting such fraudulent activities; however, they remain vulnerable to adversarial attacks that can manipulate input data to evade detection. This paper investigates the adversarial robustness of various machine learning models applied to synthetic identity fraud detection in credit risk settings. We simulate diverse adversarial attack strategies on benchmark datasets and evaluate the impact on model performance, highlighting critical vulnerabilities. Furthermore, we propose and assess defense mechanisms, including adversarial training and robust feature engineering, to enhance model resilience. Our results reveal significant trade-offs between accuracy and robustness, underscoring the need for balanced solutions tailored to financial fraud contexts. The study provides actionable insights for practitioners aiming to deploy more secure and trustworthy fraud detection systems, contributing to improved credit risk management in an increasingly adversarial environment.