"Artificial Intelligence in Accounting: Enhancing Fraud Detection and Risk Management"

Abstract

Industry regulators and enforcers demand the accounting profession transform to one that adopts more progressive models for fraud detection, prevention and risk mitigation as financial fraud becomes increasingly strategic. This adolescence explores the strategic role of false knowledge (AI) in changing fraud detection and risk management practices in accounting. By reviewing current AI applications, especially those employing machine learning models to identify anomalies or irregularities in financial data, this research assesses the potential for using these technologies to improve the timeliness and reliability of fraudulent activity detection. Now, we want to share insights from recent case studies and performance data that illustrate how AI enhances traditional audit processes and helps improve financial oversight in general. The study also discusses challenges to implementation, such as data governance, model explainability and organizational readiness. AI systems can provide tremendous potential in increasing the quality of audit, ensuring strong internal controls and facilitating proactive financial risk management, our findings indicate. The paper also presents applications you can use to incorporate such AI technologies into your fraud prevention framework, along with practical recommendations for business executives and accounting professionals.

Artificial Intelligence, Fraud Detection, Accounting, Risk Management, Machine Learning, Financial Auditing, Anomaly Detection, Internal Controls, Predictive Analytics, Financial Integrity

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