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Risk assessment is a substantial problem for financial institutions that hasbeen extensively studied both for its methodological richness and its variouspractical applications. With the expansion of inclusive finance, recentattentions are paid to micro and small-sized enterprises (MSEs). Compared withlarge companies, MSEs present a higher exposure rate to default owing to theirinsecure financial stability. Conventional efforts learn classifiers fromhistorical data with elaborate feature engineering. However, the main obstaclefor MSEs involves severe deficiency in credit-related information, which maydegrade the performance of prediction. Besides, financial activities havediverse explicit and implicit relations, which have not been fully exploitedfor risk judgement in commercial banks. In particular, the observations on realdata show that various relationships between company users have additionalpower in financial risk analysis. In this paper, we consider a graph of bankingdata, and propose a novel HIDAM model for the purpose. Specifically, we attemptto incorporate heterogeneous information network with rich attributes onmulti-typed nodes and links for modeling the scenario of business bankingservice. To enhance feature representation of MSEs, we extract interactiveinformation through meta-paths and fully exploit path information. Furthermore,we devise a hierarchical attention mechanism respectively to learn theimportance of contents inside each meta-path and the importance of differentmetapahs. Experimental results verify that HIDAM outperforms state-of-the-artcompetitors on real-world banking data.

Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users