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

• DOI: 10.54097/74414c90
• Publication Date: 2024-04-28T02:47:40Z
• Authors: Zengyi Huang; Haotian Zheng; Chen Li; Chang Che
• Keywords: K-Means Clustering

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