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

arXiv: https://arxiv.org/abs/2204.11849 · HTML: https://mlsanity.com/paper/2204.11849/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 24 Apr 2022.

## Builds on (most cited first, 16 of 16)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [DeeperGCN: All You Need to Train Deeper GCNs](https://mlsanity.com/paper/2006.07739/) (2020)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [A Survey of Heterogeneous Information Network Analysis](https://mlsanity.com/paper/1511.04854/) (2015)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [E.T.-RNN: Applying Deep Learning to Credit Loan Applications](https://mlsanity.com/paper/1911.02496/) (2019)
- [DeepTrax: Embedding Graphs of Financial Transactions](https://mlsanity.com/paper/1907.07225/) (2019)
- [R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph](https://mlsanity.com/paper/2103.07877/) (2021)
- [Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching](https://mlsanity.com/paper/2111.00926/) (2021)
- [EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data](https://mlsanity.com/paper/2009.14588/) (2020)
- [HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks](https://mlsanity.com/paper/1709.04129/) (2017)
- [Motif Enhanced Recommendation over Heterogeneous Information Network](https://mlsanity.com/paper/1908.09701/) (2019)

## Built on by (most cited first, 2 of 2)

- [Graph Learning and Its Advancements on Large Language Models: A Holistic Survey](https://mlsanity.com/paper/2212.08966/) (2022)
- [Research and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning](https://mlsanity.com/paper/2409.10331/) (2024)

## Abstract (arXiv)

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.