AttentionFM: Incorporating Attention Mechanism and Factorization Machine for Credit Scoring

Abstract

Learning effective feature interactions behind user behavior is challenging in credit scoring. Existing machine learning methods seem to have a strong bias towards low-order or high-order interactions, or require expertise feature engineering. In this paper, we present a novel neural network approach AttentionFM, which incorporates Factorization Machines and Attention mechanism for credit scoring. The proposed model focuses more on critical features and emphasizes both low- and high-order feature interactions, with no need of manually feature engineering on raw data representation. Experimental results demonstrate that our proposed model significantly outperforms the baselines based on two public datasets.