Explainable GAN Framework for Financial Auditors: Enhancing Anomaly Detection with Attention and Feature Attribution Layers

Nirup Kumar Reddy Pothireddy

doi:10.63519/IJCSERD_12_01_010

Authors

Nirup Kumar Reddy Pothireddy Independent Researcher, USA. Author

DOI:

https://doi.org/10.63519/IJCSERD_12_01_010

Keywords:

Explainable AI, Generative Adversarial Networks, Anomaly Detection, Financial Auditing, Feature Attribution, Attention Mechanisms, SHAP, Audit Transparency

Abstract

With increasing intricacy and scale of financial transactions, anomaly detection occupies the stage as one of the most significant tasks in the modern financial auditing system. However powerful traditional machine learning techniques may be, they are often not interpretable, an inherent need in financial interpretation. GANs have shown promise for capturing sophisticated fraud behaviors on highly imbalanced data; however, their black-box nature is a limiting factor for direct applications in auditing scenarios where transparency and explanations matter. This research introduces eXplainable GAN (X-GAN) architecture that implements attention mechanisms and feature attribution layers to address this challenge. It is designed not only to detect anomalous patterns in financial data but also to provide interpretations for every instance flagged about being anomalous, hence helping auditors focus on the pertinent parts of the transaction data while improving transparency as the SHAP measures account for the specific contributions of individual features.

This study has put the X-GAN through its paces to investigate its capacity on real and synthetic financial datasets, with the emphasis on comparison with standard anomaly detection approaches. On every metric, the framework is argued to improve on precision, recall, AUC, and interpretability metrics. Visual heatmaps and feature attribution scores were counted on for grounding the transparency of decisions made by the model. The study suggests that embedding explainability on a GAN model means improving accuracy while building trust and compliance-readiness -- key prerequisites for financial institutions and auditors.

This research is definitely one of the few contributions that begin to bridge the gap between modern, opaque AI systems LAI's) and the strong transparency needs of a financial audit environment. The proposed X-GAN model stands out as a milestone toward making GANs a reality in real-life financial scrutiny---that is, ensuring not just performance but also keeping regulatory lines.

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