A cost-sensitive ensemble deep forest approach for extremely imbalanced credit fraud detection

Abstract

Credit fraud detection modeling helps prevent default risks and reduce economic losses, and increasingly sophisticated methods have been designed for predicting the default probability of clients. In such problems, the fact that the class of fraud clients is much smaller than the class of good clients makes it a challenge to detect the fraud class. To minimize the financial losses in extremely imbalanced datasets, this paper delivers a novel cost-sensitive ensemble model under the framework of deep forest. The model first introduces a cost-sensitive strategy to assign a higher cost to the fraud class, thereby improving the attention of the model to the fraud samples. As everyone knows, for the basic classifiers of ensemble learning, the greater their differences, the better the performance after ensemble. So the model adds superior cost-sensitive base classifiers into the cascade structure to improve the overall performance. The model also introduces Type II error as the convergence index to automatically adjust the depth of the cascade structure. The experiments conducted on the European credit dataset and a private electronic transaction dataset are presented to demonstrate the performance of the proposed method. The results indicate that the proposed model outperforms most benchmarks in detecting fraud samples.