Case Study The Utilization of Data Mining for Early Fraud Detection in Digital Financial Services

Abstract

The rapid development of digital financial services has significantly facilitated financial transactions while also increasing the risk of fraud, which can harm both individuals and institutions. This study leverages data mining techniques for early detection of suspicious transactions in digital financial services. A classification method was employed using a financial transaction dataset from a digital platform. The analysis involved applying machine learning algorithms to build a predictive model capable of distinguishing normal transactions from suspicious ones. Results indicate that the developed model achieves high accuracy in fraud detection, making it a reliable early warning system to prevent further losses. These findings demonstrate the substantial potential of data mining in enhancing the security of digital financial services through proactive fraud detection and mitigation.