ADAPT: Adversarial Domain Adaptation with Purifier Training for Cross-Domain Credit Risk Forecasting

Recent research on transfer learning reveals that adversarial domain adaptation effectively narrows the difference between the source and the target domain distributions, and realizes better transfer of the source domain knowledge. However, how to overcome the intra/interdomain imbalance problems in domain adaptation, e.g. observed in cross-domain credit risk forecasting, is under-explored. The intra-domain imbalance problem results from the extremely limited throngs, e.g., defaulters, in both source and target domain. Meanwhile, the disparity in sample size across different domains leads to suboptimal transferability, which is known as the inter-domain imbalance problem. In this paper, we propose an unsupervised purifier training based transfer learning approach named ADAPT (Adversarial Domain Adaptation with Purifier Training) to resolve the intra/inter-domain imbalance problems in domain adaptation. We also extend our ADAPT method to the multisource domain adaptation via weighted source integration. We investigate the effectiveness of our method on a real-world industrial dataset on cross-domain credit risk forecasting containing 1.33 million users. Experimental results exhibit that the proposed method significantly outperforms the state-of-the-art methods. Visualization of the results further witnesses the interpretability of our method.