Construction of financial fraud identification model based on stacking and accounting indicators

In recent years, with financial fraud events coming one after another, the legitimate rights and interests of consumers have been seriously damaged. To solve the problem that financial fraud behaviors are more difficult to identify and more hidden, the study proposes a financial fraud identification model based on stacked integrated learning model and accounting metrics. The model adopts a multi-level feature extraction method and combines multiple base learners and logistic regression to identify financial fraud based on accounting metrics. In this study, the accuracy of the Stacking model is 85.23%, which is 5.32% higher than that of the other 5 Stacking models. Compared with other feature models, the accuracy and F1-score of the recognition model based on accounting index are increased by 18.68% and 19.11% on average. The study indicates that the financial fraud identification model based on Stacking as well as accounting metrics has good performance along with low under reporting rate for fraudulent firms. The study explores financial fraud from multiple dimensions based on annual reports, which can effectively reduce the human economic losses caused by financial fraud and assist the development of anti-fraud behavior.