Application Analysis of the Machine Learning Fusion Model in Building a Financial Fraud Prediction Model

Abstract

Financial data fraud by listed companies has brought an extremely bad impact on the market and society. Predicting the financial data fraud of listed companies in advance may reduce losses. Therefore, the key to solving the problem is to build a financial fraud prediction model. This paper analyzes the prediction and identification models of financial fraud at home and abroad in detail, and finds the problems existing in these prediction models. In view of these shortcomings, this paper proposes to build a financial fraud prediction model based on a machine learning fusion model. The first is the unbalanced processing of data samples. The oversampling method is used to improve the model prediction effect by setting a reasonable sampling ratio. Then, four machine learning models (GBDT, random forest, support vector machine, and decision tree) are selected suitable for financial data. The training set is used to optimize the hyperparameters of the four machine learning models separately. This paper proposes integrating the random search and grid search mechanisms to adjust the parameters to the optimum. Finally, a financial fraud prediction model is constructed based on the multimodel fusion of the integrated learning framework. First, the base learner integrates the predicted results of the four models and performs five-fold crossvalidation on the training set. The meta-learner then uses the GBDT model to train integrated data from the first layer, resulting in a fusion model. The experimental results show that the AUC value of the fusion model is significantly higher than that of the single model. Therefore, the fusion model proposed in this paper can effectively improve the prediction effect.