Fraud detection in banking transactions using artificial intelligence

Abstract

The article analyzes the impact of Artificial Intelligence (AI) in the banking and financial sector, specifically in the detection of fraud in financial transactions. It highlights the evolution of AI and its transformative role in the banking and financial context. The central focus is on the complexity of financial frauds, addressing various types and their economic impact. Emblematic cases such as the Jerome Kerviel's fraud at Société Générale (2008) and Nick Leeson's Fraud at Barings Banks (1995) are mentioned, underscoring the need for effective methods in preventing and identifying these incidents. The article outlines modern techniques and AI employed in detecting banking frauds, emphasizing the use of advanced algorithms such as machine learning and neural networks. The proposal is to use these approaches to identify suspicious patterns in financial transactions, emphasizing not only their effectiveness but also their potential in mitigating risks and protecting financial institutions against fraudulent activities. The study provides a detailed overview from older to more current AI methods, highlighting the use of advanced algorithms to achieve more precise and efficient detection. The results underscore the capability of these methods in identifying and preventing incidents similar to the cases presented. It is expected that advanced algorithms will provide an effective response in identifying suspicious transactions. The study concludes by emphasizing not only the effectiveness of modern AI approaches in detecting banking frauds but also the potential of these techniques in protecting financial institutions against fraudulent activities. It highlights the ongoing importance of developing and implementing advanced AI methods to strengthen security in the banking sector, mitigating risks, and safeguarding clients and institutions against financial threats.