A Machine Learning Framework for Adaptive FinTech Security Provisioning

Abstract

FinTech services bring an elevated level of security concerns due to the non-conventional characteristics such as diverse and evolving transaction models. Hence, conventional financial security provisioning approaches have limited applicability, rather, it requires more effective, intelligent, and reactive anomaly management for FinTech transactions. We present a comprehensive framework for managing FinTech transactions which utilizes machine learning-based intelligence in deriving anomaly detection models and adaptive FinTech security provision. And, we define a formal model of the anomaly management, and present a software framework implementing the model.