AI-DRIVEN FORENSIC ACCOUNTING FOR FRAUD PREVENTION AT EY INDIA

Abstract

AI-driven forensic accounting in strengthening fraud prevention tactics at EY India is examined in this essay. It examines how investigations and audits have been altered by contemporary analytics, machine learning algorithms, and intelligent automation. The paper demonstrates how EY use techniques for anomaly detection, predictive risk assessment, and continuous transaction monitoring to identify issues early. Additionally, it examines how AI enhances forensic procedures by decreasing human error, expediting evidence gathering, and verifying audit trail accuracy. The study demonstrates the significance of natural language processing in identifying covert fraud indicators in relationships, contracts, and unstructured data. It also examines how EY India use AI-powered screens that assist decision-making by utilizing risk heatmaps and pattern visualization. The study demonstrates how crucial it is to integrate subject-matter expertise with AI to ensure that red signals are interpreted appropriately. Additionally, it discusses data stewardship, model transparency, and ethical issues that are critical to the proper application of AI. The findings demonstrate how AI greatly facilitates firms' ability to identify fraud and ensure proper financial management.