Application of Machine Learning in Credit Risk Scorecard

Lee, Choon Yi; Koh, Siew Khew; Lee, Min Cherng; Pan, Wei

doi:10.1007/978-981-16-7334-4_29

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…The IV of a variable shows its strength as a predictor and is calculated as the weighted sum of the WOE values for all categories of that variable. The weight of each category is determined based on the difference between its frequency among events and non-events [44]. We use the logistic regression LASSO method to select a maximum of 10 variables from 15 variables with the highest IV.…”

Section: Methodsmentioning

confidence: 99%

Risk score models for urinary tract infection hospitalization

Alizadeh,

Vahdat,

Shashaani

et al. 2024

PLoS ONE

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Annually, urinary tract infections (UTIs) affect over a hundred million people worldwide. Early detection of high-risk individuals can help prevent hospitalization for UTIs, which imposes significant economic and social burden on patients and caregivers. We present two methods to generate risk score models for UTI hospitalization. We utilize a sample of patients from the insurance claims data provided by the Centers for Medicare and Medicaid Services to develop and validate the proposed methods. Our dataset encompasses a wide range of features, such as demographics, medical history, and healthcare utilization of the patients along with provider quality metrics and community-based metrics. The proposed methods scale and round the coefficients of an underlying logistic regression model to create scoring tables. We present computational experiments to evaluate the prediction performance of both models. We also discuss different features of these models with respect to their impact on interpretability. Our findings emphasize the effectiveness of risk score models as practical tools for identifying high-risk patients and provide a quantitative assessment of the significance of various risk factors in UTI hospitalizations such as admission to ICU in the last 3 months, cognitive disorders and low inpatient, outpatient and carrier costs in the last 6 months.

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Section: Methodsmentioning

confidence: 99%

Risk score models for urinary tract infection hospitalization

Alizadeh,

Vahdat,

Shashaani

et al. 2024

PLoS ONE

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Research on PPP Enterprise Credit Dynamic Prediction Model

et al. 2022

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The debt default risk of local government financing vehicles (LGFVs) has become a potential trigger for systemic financial risks. How to effectively prevent hidden debt risk has always been a hot issue in public-private partnership (PPP) financing management research. In recent years, machine learning has become more and more popular in the study of enterprise credit evaluation. However, most scholars only focus on the output of the model, and do not explain in detail the extent to which variables affect the model and the decision-making process of the model. In this paper, we aim to apply a better credit rating method to the key factors and analysis of LGFV’s default risk, and analyze the decision-making process of the model in a visual form. Firstly, this paper analyzes the financial data of LGFVs. Secondly, the XGBoost-logistic combination algorithm is introduced to integrate the typical characteristics of PPP projects and construct the credit evaluation model of LGFVs. Finally, we verify the feasibility of the model by K-fold cross validation and performance evaluation. The results show that: (1) net worth, total assets, operating income, and return on equity are the most critical factors affecting the credit risk of LGFVs, asset-liability ratio and tax revenue are also potentially important factors; (2) the XGBoost-logistic model can identify the key factors affecting the credit risk of LGFVs, and has better classification performance and predictive ability. (3) The influence of each characteristic variable on model decision can be quantified by the SHAP value, and the classification decision visualization of the model improves the interpretability of the model.

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Application of Machine Learning in Credit Risk Scorecard

Cited by 2 publications

References 18 publications

Risk score models for urinary tract infection hospitalization

Risk score models for urinary tract infection hospitalization

Research on PPP Enterprise Credit Dynamic Prediction Model

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