CONTINUOUS AUDITING AND EXPLAINABLE AI FOR ENHANCING REAL TIME FINANCIAL ANALYSIS

Abstrak

The incorporation of machine learning (ML) into modern financial analysis has made transactions more complex and ondemand, and further increased the scope of ML applications in finance. On the other hand, accounting and auditing processes have yet to adopt machine learning systems due to challenges of precision, interpretability, and integration. This research analyzes the balance between accuracy and explainability in XAI for fraud detection with XGBoost, Transformer-Based Models, and continuous auditing approaches. Key findings suggest that although less preferred, Transformer-Based Models are more accurate in detecting multi-faceted fraud and deliver an AUC-ROC of 95%. XGBoost, with an AUC-ROC score of 92%, surpasses set benchmarks for continuous auditing, achieving high assurance while requiring low operational complexity, and therefore the model with fewer continuous auditing constraints. The results substantiate the premise that claiming compliance with audit requirements evokes low complexity emerges logic steeped in trust faced by agile controllers. The primary claim was the accompanied loss of understanding with defining accuracy and the adoption scrutiny processes of XGBoost. These results emphasize the potential of hybrid AI systems achieved by merging explainability of XGBoost with sequential analysis of Transformers which also tend to be less interpretable. Such models could benefit decision makers significantly.