"StackTraceXAI - Explainable Hybrid Anomaly Detection for Financial Fraud in ERP Systems "

Abstract

Enterprise Resource Planning (ERP) systems are essentially the backbone of modern businesses, playing a crucial role in managing their daily operations. Yet, this complex and high-volume nature of financial transactions observed within an ERP system on a daily basis makes them a prime target for numerous types of fraud and anomalies. Effective detection and subsequent transparent analysis are crucial in mitigating potential losses and operational disruptions to business operations. Within this paper, the author proposes a novel fraud detection framework leveraging a Variational Autoencoder (VAE) working in tandem with an XGBoost (XGB) model for anomaly identification, detection and enhanced interpretability. The VAE learns a compressed representation of normal transaction patterns, enabling the identification of deviations indicative of anomalies. Subsequently, XGBoost, a supervised learning algorithm, refines these anomaly scores, providing a more accurate classification. Furthermore, the framework incorporates Explainable AI (XAI) techniques to enhance transparency and interpretability.