Application of Real Time Machine Learning Models in Financial Fraud Identification

Abstract

With the continuous advancement of digital and intelligent technologies, financial fraud methods are becoming more diversified, making it difficult for traditional rule engines and manual auditing methods to keep up with the speed of change and achieve accurate and real-time detection standards. At this point, efficient and self-learning real-time machine learning algorithms have become important tools for identifying financial fraud. This algorithm can conduct in-depth analysis of massive amounts of data generated by financial transactions, detect suspicious fraudulent activities in real time, and respond quickly by issuing alerts. This article focuses on the characteristics of machine learning models and deeply analyzes their application in financial fraud recognition, covering multiple aspects such as data preprocessing, feature engineering, model selection and optimization. The actual test results show that the experimental results validate that the real-time machine learning model exhibits excellent performance in accuracy, processing speed, and adaptability, bringing more advanced and intelligent anti fraud technology to the financial industry.