Evaluating the credit risk of SMEs using legal judgments

Yin, Chang; Jiang, Cuiqing; Jain, Hemant; Zhao, Wang

doi:10.1016/j.dss.2020.113364

Cited by 47 publications

(37 citation statements)

References 34 publications

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“…Inspired by [46], we divide the duration of action (DOA) into two types, i.e. less than two years and more than two years.…”

Section: Duration Of Actionmentioning

confidence: 99%

“…As the most recent two years' lawsuits play an important role for enterprise risk prediction [46], we assign lower 𝑤 when performing time weight decay for the recent two years' lawsuits. We divide lawsuits into 𝐿 periods {𝑇 1 ,𝑇 2 , ...,𝑇 𝐿 }.…”

Section: 𝑔(δmentioning

confidence: 99%

“…However, SMEs usually lacks normal financial reports as well as public conference calls, which brings a challenge to risk analysis for SMEs. On the other hand, there are abundant risk sources such as relevant lawsuits, which are proved significantly related to enterprise credit risk [46], which have not been well utilized in previous works. Enterprise spillover risk also plays a vital role in risk analysis as there is no enterprise absolute independent from others.…”

Section: Related Workmentioning

confidence: 99%

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Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks

Yu¹,

Sang²,

Yu³

et al. 2022

Preprint

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Bankruptcy risk prediction for Small and Medium-sized Enterprises (SMEs) is a crucial step for financial institutions to make the loan decision and identify region economics's early warning. However, previous studies in both finance and AI research fields only consider either the intra-risk or the spillover-risk, ignoring their interactions and their combinatorial effect for simplicity. This paper for the first time considers both risks simultaneously and their joint effect in bankruptcy prediction. Specifically, we first propose an enterprise intra-risk encoder with LSTM based on enterprise risk statistical significance indicators from its basic business information and litigation information for its intra-risk learning. Afterward, we propose an enterprise spillover-risk encoder based on enterprise relational information from the enterprise knowledge graph for its spillover-risk embedding. In particular, the spillover-risk encoder is equipped with both the newly proposed Hyper-Graph Neural Networks (Hyper-GNNs) and Heterogeneous Graph Neural Networks (Heter-GNNs), which is able to model spillover risk from two different aspects, i.e. common risk factors based on hyperedges and direct diffusion risk from the neighbors, respectively. With the two kinds of encoders, a unified framework is designed to simultaneously capture intra-risk and spillover-risk for bankruptcy prediction. To evaluate our model, we collect multi-sources SMEs real-world data and build a novel benchmark dataset SMEsD. We provide open access to the dataset, which is expected to promote the financial risk analysis research further. Experiments on SMEsD against nine SOTA baselines demonstrate the effectiveness of the proposed model for bankruptcy prediction.

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“…Inspired by [46], we divide the duration of action (DOA) into two types, i.e. less than two years and more than two years.…”

Section: Duration Of Actionmentioning

confidence: 99%

Section: 𝑔(δmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks

Yu¹,

Sang²,

Yu³

et al. 2022

Preprint

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show abstract

“…Mazanai and Fatoki (2012) apprise that four factors are considered as key factors that banks are reluctant to lend to SMEs. These are the problem of information asymmetry (Yin et al, 2020), the operating costs of small loans are very high (Do et al, 2019), Collateral issues (Xu, 2019), and perceived high risk of SMEs (Osano & Languitone, 2016). The above-mentioned reasons applied to both developing and developed economies.…”

Section: Inmentioning

confidence: 99%

Critical Factors Associated With the Access to Bank Credit: An Exploratory Study

Elahi

Ahmed

Majid

et al. 2021

HSSR

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Purpose of the study: The purpose of the current study is to get subjective information from SMEs about their views and elicit the experience of getting loans and why not getting loans. Methodology: This study has been used qualitative research and findings have been gathered using face-to-face in-depth semi-structured interviews. Main Findings: Findings showed that there are two types of SME firms categorized in 1) Firms that are willing to take a loan but unable to get them and 2) Firms that have access to finance but don’t want to obtain them, different themes have been extracted from the aforementioned categorization. Applications of this study: This research can be effective to develop an economy in terms of employment generation, human resource development, value addition, poverty alleviation, income equality, entrepreneurial culture, and innovation, and Pakistan is no exception in this regard. Novelty/Originality of this study: The study is contributing to running a manufacturing unit, usually a large land area is required. Moreover, business locations are preferred near to markets for ease of doing business. Since all these preferred choices require a lot of money to incur for capital expenditure which is hardly available for SMEs.

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“…Machine learning has begun to revolutionize various industries already and it would aid India"s legal system in producing legal judgments with higher accuracy and precision. If law firms/advocates in India use this AI application for risk assessment for an out-of-court settlement/Alternative Dispute Resolution (ADR) mechanism, the number of cases would reduce and faster justice could be provided [25] [26]. In India, legal search engines use Artificial Intelligence for legal analytics and visualization through its Case Map and Taxonomy and it also provides an analysis of judge"s disposition through data analyzed by AI.…”

Section: Introductionmentioning

confidence: 99%

A Meta Analysis of Attention Models on Legal Judgment Prediction System

Sukanya¹,

J.Priyadarshini²

2021

IJACSA

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Intelligence in legal research is transforming the legal area in manifold ways. Pendency of court cases is a long-lasting problem in the judiciary due to various reasons such as lack of judges, lack of technology in legal services and the legal loopholes. The judicial system has to be more competent and more reliable in providing justice on time. One of the major causes of pending cases is the lack of legal intelligence to assist the litigants. The study in this paper reviews the challenges faced by judgment prediction system due to lengthy case facts using deep learning model. The Legal Judgment prediction system can help lawyers, judges and civilians to predict the win or loss rate, punishment term and applicable law articles for new cases. Besides, the paper reviews current encoding and decoding architecture with attention mechanism of transformer model that can be used for Legal Judgment Prediction system. Natural Language Processing using deep learning is an exploring field and there is a need for research to evaluate the current state of the art at the intersection of good text processing and feature representation with a deep learning model. This paper aims to develop a systematic review of existing methods used in the legal judgment prediction system and about the Hierarchical Attention Neural network model in detail. This can also be used in other applications such as legal document classification, sentimental analysis, news classification, text translation, medical reports and so on.

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Evaluating the credit risk of SMEs using legal judgments

Cited by 47 publications

References 34 publications

Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks

Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks

Critical Factors Associated With the Access to Bank Credit: An Exploratory Study

A Meta Analysis of Attention Models on Legal Judgment Prediction System

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