Citation:

Sachin Kumar Gupta, 2026. AI-Driven Continuous Auditing: Enhancing Risk Assessment and Control Testing   Volume 6 Issue 2: 135-140.

Abstract:

This paper presents a design-and-evaluation study of an AI-Driven Continuous Auditing (AID-CA) framework for real-time risk assessment and control testing. Building on a focused narrative synthesis of prior continuous auditing and AI-in-audit research, we design a multi-layer assurance architecture and empirically evaluate a hybrid deep-learning/ensemble model. Experiments are conducted on semi-real enterprise transaction streams constructed from anonymized ERP logs augmented with rule-consistent synthetic anomalies (≈2.1 million labeled records).

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Keywords:

AI-Driven Continuous Auditing, Continuous Assurance, Risk Assessment, Control Testin,Hybrid AI Models, Explainable AI, Audit Analytics