Comprehensive Audit Data Pipeline Architecture - Strategies for Modern Banking Audit, Compliance and Risk Management

Abstract

The Institution Bank, which has over $2 trillion in assets, is one of the largest Bank & Financial Services Organizations in the world, classified as one of the largest Global Systemically Important Banks (GSIBs). Strong auditing capabilities are critical for managing an organization's risks and ensuring compliance with regulations and operating effectively. In this session, we will describe a holistic audit data pipeline that combines Audit Data Pipeline Architecture, Audit Data Analytics (using Python and Alteryx), and the overall direction of the IIA standards as specified by the Institute of Internal Auditors. The architecture incorporates machine learning for predictive analytics, multi-source data intake, and transformation through ETL and the identification of the conflicts of interest. Performance metrics metrics such as throughput, latency, error rates, and data quality are considered to improve the performance of the pipeline. Visualizations and example datasets demonstrate effective audit monitoring workflows. The structured Audit Data Pipeline Architecture provides the audit function a comprehensive structure to find control deficiencies and meet continuous monitoring expectations within banking environments while meeting regulatory expectations of G-SIBs. Recommendations for future improvement of the pipeline to improve controls amidst the changing financial landscape, include real-time streaming, advanced anomaly detection techniques, explainable AI Models, and blockchain-based audit trails.