Adaptive Machine Learning Models for Financial Fraud Detection in Digital Transactions

Abstract

The rise of digital payments has increased the risk of financial fraud. This study introduces an adaptive machine learning model that combines ensemble learning techniques with real-time transaction monitoring. The system dynamically updates its parameters based on evolving fraud patterns, improving detection accuracy. Results show that the proposed model reduces false positives while maintaining high detection rates, offering a robust solution for modern financial systems.