Harnessing artificial intelligence in financial fraud detection and prevention systems

Abstract

In the current financial environment, risk management and banking fraud prevention are crucial, and artificial intelligence (AI) holds enormous promise for improving these fields. Fraud has grown to be a major concern and a global occurrence. It exists in every nation and impacts all kinds of organizations, regardless of their size, industry, or level of profitability. The main goal of this paper is to provide readers with a thorough overview of the literature on corporate fraud so they can comprehend "why" fraud happens and "how" to stop it. We then thoroughly examine the accepted data mining approaches, which include using artificial intelligence algorithms and machine learning-based model recognition, as well as data pre-treatment and feature engineering in big data environments. Fraud prediction, identification verification, and transaction monitoring are examples of real-world applications. This chapter, which focuses on Explainable AI, explores the transparency of AI-driven decisions, which is essential for tackling issues like algorithmic biases and data privacy. Keeping up with fraudsters requires constant innovation. In the end, using AI promises to protect resources and increase confidence in financial institutions. The study emphasizes the advantages of AI-powered lie detection, such as enhanced effectiveness, better precision, and proactive risk reduction. Nonetheless, obstacles such as technological constraints and regulatory factors are acknowledged. Finally, we look at how AI and ML have the potential to transform the financial crime prevention landscape.