The generalized Vasicek credit risk model: A Machine Learning approach

Machine learning applied to financial transaction records can predict how likely a small business is to repay a loan. For this purpose we compared a traditional scorecard credit risk model against various machine learning models and found that XGBoost with monotonic constraints outperformed scorecard model by 7% in K-S statistic. To deploy such a machine learning model in production for loan application risk scoring it must comply with lending industry regulations that require lenders to provide understandable and specific reasons for credit decisions. Thus we also developed a loan decision explanation technique based on the ideas of WoE and SHAP. Our research was carried out using a historical dataset of tens of thousands of loans and millions of associated financial transactions. The credit risk scoring model based on XGBoost with monotonic constraints and SHAP explanations described in this paper have been deployed by QuickBooks Capital to assess incoming loan applications since July 2019.

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An Interpretable Automated Machine Learning Credit Risk Model

Communications in Computer and Information Science - Applied Computer Sciences in Engineering ◽

10.1007/978-3-030-61834-6_2 ◽

2020 ◽

pp. 16-23

Author(s):

Gabriel Patron ◽

Diego Leon ◽

Edwin Lopez ◽

German Hernandez

Keyword(s):

Machine Learning ◽

Credit Risk ◽

Risk Model ◽

Automated Machine Learning ◽

Credit Risk Model

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A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

Modern Physics Letters B ◽

10.1142/s0217984917400553 ◽

2017 ◽

Vol 31 (19-21) ◽

pp. 1740055 ◽

Cited By ~ 4

Author(s):

Jiang Xie ◽

Yan Liu ◽

Xu Zeng ◽

Wu Zhang ◽

Zhen Mei

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Type 2 Diabetes ◽

Electronic Health Records ◽

Physical Examination ◽

Risk Model ◽

Learning Approach ◽

Health Records ◽

Machine Learning Approach

An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

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