The Application of Machine Learning in Financial Fraud Analysis

Against the backdrop of the increasingly severe problem of financial fraud, this study is dedicated to constructing an efficient fraud detection model. First, exploratory data analysis is employed to analyze financial data. Analyses of univariate and multivariate variables are carried out to gain insights into the distribution and correlation characteristics of different features. Subsequently, data preprocessing is performed on continuous and categorical variables to improve data quality and usability. On this basis, machine learning models such as logistic regression, decision trees, random forests, and Support Vector Machine (SVM) are utilized for modeling. After the model training is completed, a series of model metrics, such as accuracy, recall, and F1-score, are used to comprehensively evaluate the prediction performance of the models. The experimental results demonstrate that different models exhibit varying advantages and limitations in the task of financial fraud prediction, providing important references for subsequent model optimization and practical applications. The findings of this study are expected to assist financial institutions in enhancing their fraud prevention capabilities and effectively reducing losses caused by fraud risks.