Risk assessment is a substantial problem for financial institutions that has
been extensively studied both for its methodological richness and its various
practical applications. With the expansion of inclusive finance, recent
attentions are paid to micro and small-sized enterprises (MSEs). Compared with
large companies, MSEs present a higher exposure rate to default owing to their
insecure financial stability. Conventional efforts learn classifiers from
historical data with elaborate feature engineering. However, the main obstacle
for MSEs involves severe deficiency in credit-related information, which may
degrade the performance of prediction. Besides, financial activities have
diverse explicit and implicit relations, which have not been fully exploited
for risk judgement in commercial banks. In particular, the observations on real
data show that various relationships between company users have additional
power in financial risk analysis. In this paper, we consider a graph of banking
data, and propose a novel HIDAM model for the purpose. Specifically, we attempt
to incorporate heterogeneous information network with rich attributes on
multi-typed nodes and links for modeling the scenario of business banking
service. To enhance feature representation of MSEs, we extract interactive
information through meta-paths and fully exploit path information. Furthermore,
we devise a hierarchical attention mechanism respectively to learn the
importance of contents inside each meta-path and the importance of different
metapahs. Experimental results verify that HIDAM outperforms state-of-the-art
competitors on real-world banking data.

本文提出了一种名为 HIDAM 的新模型，以应对金融机构面临的风险评估问题，特别是针对小微企业的金融风险问题。该模型应用异质信息网络，提取元路径中的交互信息，并采用分层注意机制来学习不同元路径内部的内容和不同元路径之间的重要性，从而在真实世界的银行数据上取得了比其他竞争者更好的性能表现。