Safeguarding FinTech innovations with machine learning: Comparative assessment of various approaches

Abstract

Protecting data is paramount to the development of FinTech. Fraudulent activities can exploit weaknesses in FinTech systems, wreaking havoc on both customers and service providers. However, machine-learning approaches have the potential to spot irregularities in FinTech systems, looking for red flags in economic data sets and using such red flags to inform predictive models for the detection of future fraud. We assess anomaly detection techniques, thereby adding to this crucial topic. We apply a variety of techniques to multiple synthetic and real-world databases. Findings corroborate that machine-learning approaches help with fraud detection, although with varying degrees of effectiveness. Our findings demonstrate that competitive advantage is the most crucial component amongst some Fintech-based predictors, while sales volume is diagnosed as having the least effective importance. To ensure the consistency and accuracy of our findings, we choose case studies for evaluating ML-based fraudulent activities based on the availability of properly allowed appropriate.