A Corporate Financial Fraud Detection Method Based on Transaction Behavior Representation Learning
Inter-company related transactions have become a common way for executing financial fraud.Traditional quantitative analysis methods treat each company as an independent entity,failing to explore the complex relationships a-mong the transaction parties.To address this,we propose a corporate financial fraud detection method based on mixed transaction behavior features and an ensemble machine learning model.First,a knowledge graph is constructed to extract related transaction features from corporate transaction data,which are then integrated with common financial features.Subsequently,we introduce an ensemble framework for financial fraud detection that combines Decision Tree(DT),Tandom Forest(RF),and Adaboost algorithms.The hybrid features serve as input,and the sub-models vote on whether each transaction is fraudulent,reaching a final decision through hard or soft voting aggregation methods.Experi-ments on real transaction datasets of listed companies demonstrate that the hybrid features enhance financial fraud detec-tion performance.The proposed framework achieves a 92.46%AUC in detecting fraudulent transactions by integrating diverse models and various voting mechanisms,significantly outperforming individual classifiers.This method facilitates sustainable corporate growth and assists regulatory agencies in maintaining market order.
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