Role of Artificial Intelligence and Machine Learning in Fraud Detection and Prevention

Abstract

In today's rapidly advancing technological landscape and expanding economies, the financial sector confronts a pressing issue: the escalating prevalence of fraud. Annual losses in the hundreds of billions of dollars afflict financial institutions and clients globally due to fraudulent activities. Fraudsters continuously refine their tactics, targeting the financial industry with schemes ranging from credit card fraud to insider trading. Despite geographical boundaries, fraud affects individuals and businesses worldwide. Machine learning (ML) and artificial intelligence (AI) emerge as indispensable tools in this battle, particularly in anomaly detection. While supervised ML models dominate, challenges persist, prompting a shift towards semi-supervised and unsupervised learning methods. These adaptable models learn from unlabelled data, aiding in fraud detection. ML and AI empower organizations to proactively mitigate fraud risks by analysing vast datasets, identifying patterns, and pre-empting potential threats in real-time, ensuring the safeguarding of operations and stakeholders amidst evolving fraud landscapes.