Adaptive Machine Learning for Emerging Fraud Patterns in FinTech Transactions

Abstract

Adaptive learning of new fraud behavior among financial transactions on the FinTech platform has become a research urgency due to an increasing level of digitization of the global financial ecosystem. The increased use of digital banking, mobile payments, online lending and Cryptocurrency services has not only made innovation opportunities unsurpassed, but it has exposed financial platforms to more sophisticated fraudulent activities. Machine learning has emerged as one of the enabling factors to detect frauds as its pattern recognition, anomaly identification, and predictive analytics capabilities offer the solution to complex fraud situations in real-time. Categorization and detection through traditional methods such as the logistic regression, decision trees and support system machine have been applied widely to give meaning and trustworthiness to structured information. However, the evolution of fraudulent schemes has driven the adoption of the state-of-the-art and deep-learning techniques, including recurrent neural networks, long short-term memory models, graph neural networks, and transformers that encourage flexibility and large scale. Additionally, reinforcement learning, transfer learning and hybrid ensemble model can offer dynamic solutions that can be able to detect new trends of fraud in various transaction environments. All of these adaptive machine learning approaches contribute to a higher level of protection against fraud, financial stability, and assuring the confidence of consumers, therefore, they are essential in ensuring integrity and resilience of FinTech.