The invention provides a financial counterfeiting
risk detection method and device, and aims to solve the problems of strong rule dependence, insufficient
feature mining and limited detection effect in the prior art. The method comprises seven steps of sample screening,
feature design,
data set construction, data preprocessing, feature calculation, model construction and
risk detection. By designing three-dimensional features of opportunities, motivations and traces and combining financial and non-financial data, multi-dimensional
feature extraction and calculation are carried out, and the potential law of financial counterfeiting is comprehensively reflected.
Machine learning algorithms such as
random forest are adopted for model training, parameters are optimized through grid search and nested
cross validation, and the accuracy and generalization ability of the model are improved. And finally, performing
risk detection on the enterprise financial report by using the model, generating a classification
label or a risk
score, and providing data support for enterprise audit and external supervision. The method can be widely applied to the fields of financial fraud detection, tax examination and the like, and has good practicability and popularization value.