Fraud Detection and Risk Management Using AI in Business Intelligence

Author:

Ramachandran M. Sabari1,Sajithabanu S.1ORCID,Ponmalar A.2,Mohamed Sithik M.3,Jose Anand A.4ORCID

Affiliation:

1. Mohamed Sathak Engineering College, India

2. R.M.K. Engineering College, Chennai, India

3. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India

4. KCG College of Technology, India

Abstract

Fraudulent activities present significant challenges to organizations across various sectors, necessitating advanced techniques for detection and mitigation. Leveraging AI in BI offers promising solutions to enhance fraud detection capabilities and minimize risks effectively. It emphasizes importance of fraud detection and risk management strategies for safeguarding organizational assets, maintaining trust with stakeholders, The role of AI in BI focuses on machine learning techniques, deep learning approaches, and real-time fraud detection systems. Advanced techniques for fraud detection, including feature engineering, model evaluation, and explainable AI, and practical applications of AI-powered fraud detection and risk management in financial services, e-commerce, retail, and cybersecurity are illustrated through case studies. The chapter concludes by outlining future directions and emerging trends in AI, BI, and fraud detection, emphasizing importance of collaboration, ethical considerations, and knowledge sharing in addressing evolving challenges and opportunities.

Publisher

IGI Global

Reference30 articles.

1. Association of Certified Fraud Examiners (ACFE). (2023). Report to the Nations: 2023 Global Study on Occupational Fraud and Abuse.

2. BlackH. C. (2022). Black’s Law Dictionary (11th ed.). Thomson Reuters.

3. Artificial intelligence in finance and accounting: Applications and issues.;I.Bose;Journal of Finance and Accountancy,2021

4. The use of the area under the ROC curve in the evaluation of machine learning algorithms

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