Application of Machine Learning-Based K-means Clustering for Financial Fraud Detection

Abstract

In today's increasingly digital financial landscape, the frequency and complexity of fraudulent activities are on the rise, posing significant risks and losses for both financial institutions and consumers. To effectively tackle this challenge, this paper proposes a machine learning-based K-means clustering method to enhance the accuracy and efficiency of financial fraud detection. By clustering vast amounts of financial transaction data, we can identify anomalous patterns and behaviors in a timely manner, thereby detecting potential fraud. Compared to traditional rule-based detection methods, machine learning-based approaches better adapt to ever-evolving fraud techniques and patterns while improving flexibility and precision in detection. Moreover, K-means clustering also aids in optimizing resource allocation within financial institutions by enabling focused monitoring and prevention efforts in high-risk areas, thus effectively mitigating the impact of fraud on the overall financial system. In summary, the machine learning-based K-means clustering method holds promising prospects for application in the field of financial fraud detection as it strives to establish a more secure and reliable transaction environment for the finance industry.