Explainable AI in Big Data Fraud Detection

Explainability is critical for building trust in automated fraud detection systems. As financial institutions and insurance companies increasingly rely on machine learning to make high-stakes decisions about customers, the ability to explain why a transaction was flagged as fraudulent or why a claim was denied becomes essential for regulatory compliance and customer trust.

Current explanation methods like LIME and SHAP have significant limitations when processing massive datasets. While these popular explainability techniques work well in controlled settings, the paper identifies that scaling them to handle the volume and speed of real-world Big Data fraud detection remains an unsolved challenge, particularly for systems that need to provide explanations in real-time.

Different types of fraud detection models require different explanation approaches. The paper highlights that graph-based fraud detection (which maps networks of suspicious relationships) and temporal models (which track patterns over time) are particularly difficult to explain using standard methods, suggesting that one-size-fits-all explainability solutions are insufficient.

Human feedback should be integrated into explainable AI systems for fraud detection. Rather than treating explanations as a one-way communication from AI to users, the paper proposes that fraud detection systems should incorporate human expertise and feedback to continuously improve both the accuracy of fraud detection and the quality of explanations provided.

Standardized methods for evaluating explainability in fraud detection are urgently needed. The paper identifies a significant research gap: there are currently no agreed-upon standards for measuring whether explanations are actually helpful, trustworthy, or sufficient for regulatory purposes in fraud detection contexts.