  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.
