Research on Big Data Financial Fraud Detection System Based on Machine Learning

To solve the problem of fraud detection in financial transaction data, this paper proposes an innovative detection scheme that integrates machine learning and big data analysis technology. We combine the subtleties of time series analysis with the power of modern data analysis to design a model that is both efficient and accurate in identifying financial fraud. First, advanced data acquisition technology is used to capture high-frequency financial transaction data in real time to ensure the freshness and integrity of data. Then, the ARIMA (autoregressive integral moving average) model is used to analyze these time series data in depth to reveal the hidden information behind the trading patterns and trends. On this basis, the LightGBM (Lightweight Gradient Elevator) algorithm is introduced, which is a machine learning tool that performs well when dealing with high-dimensional, sparse data, especially for complex data structures in the financial sector. By integrating the time series features mined by ARIMA with multi-dimensional information such as credit history and geographical location of both parties, as the input of LightGBM model, we construct a multi-modal fraud detection framework. Empirical research shows that compared with using ARIMA model alone or traditional machine learning methods, our hybrid model has achieved a significant breakthrough in the accuracy and response speed of fraud detection, which can not only perceptively capture abnormal transaction activities, but also proactively warn potential financial risk points.