Abstract
Fraud detection systems play an increasingly pivotal role in the world of digital business transactions. As the business world embraces digital platforms, industries ranging from finance and banking to insurance and e-commerce are exposed to sophisticated fraudulent activities[1]. The ability to detect and prevent fraudulent transactions has become not just a security measure but a determinant of business success. Automated fraud detection systems stand at the forefront of this fight, identifying potential fraudulent behavior and mitigating risks. A cornerstone of these fraud detection systems is machine learning, an AI-driven technique where algorithms learn to make decisions based on patterns in data. Machine learning models are designed to differentiate between legitimate transactions and potential fraud, thus allowing businesses to flag and handle suspicious activities effectively. These models require data to learn from; the more comprehensive, varied, and representative the data, the more effectively the models can identify patterns and make accurate predictions. However, obtaining a vast and representative dataset for fraud detection presents a two-fold challenge.
Keywords