Application of Computer Technology in Financial Investment

TL;DR

This paper explores the use of data mining and machine learning models, including logistic regression, for fraud detection in financial transactions, demonstrating the effectiveness of computer technology in enhancing financial investment security.

Contribution

It introduces an empirical approach using data mining techniques and multiple classifiers to detect fraud in financial transactions, addressing class imbalance issues.

Findings

Logistic regression achieved the highest recall, F1 score, and AUC among tested models.

Data preprocessing steps improved model performance and detection accuracy.

The study confirms the practicality of computer-based data mining methods in financial fraud detection.

Abstract

In order to understand the application of computer technology in financial investment, the author proposes a research on the application of computer technology in financial investment. The author used user transaction data from a certain online payment platform as a sample, with a total of 284908 sample records, including 593 positive samples (fraud samples) and 285214 negative samples (normal samples), to conduct an empirical study on user fraud detection based on data mining. In this process, facing the problem of imbalanced positive and negative samples, the author proposes to use the Under Sampling method to construct sub samples, and then perform feature scaling, outlier detection, feature screening and other processing on the sub samples. Then, four classification models, logistic regression, K-nearest neighbor algorithm, decision tree, and support vector machine, are trained on the processed sub samples. The prediction results of the four models are evaluated, and the results show that the recall rate, Fl score, and AUC value of the logistic regression model are the highest, indicating that the detection method based on computer data mining is practical and feasible.