Example dependent cost sensitive learning based selective deep ensemble model for customer credit scoring. [PDF]
Sci RepXiao J +5 more
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JointLIME: An interpretation method for machine learning survival models with endogenous time-varying covariates in credit scoring. [PDF]
Risk AnalChen Y, Calabrese R, Martin-Barragan B.
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Enhancing credit scoring accuracy with a comprehensive evaluation of alternative data. [PDF]
PLoS OneHlongwane R, Ramaboa KKKM, Mongwe W.
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Large unbalanced credit scoring using Lasso-logistic regression ensemble. [PDF]
PLoS One, 2015 Wang H, Xu Q, Zhou L.
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A novel framework for enhancing transparency in credit scoring: Leveraging Shapley values for interpretable credit scorecards. [PDF]
PLoS OneHlongwane R, Ramabao K, Mongwe W.
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Comprehensive credit scoring datasets for robust testing: Out-of-sample, out-of-time, and out-of-universe evaluation. [PDF]
Data BriefMushava J, Murray M.
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Dealing with flawed items in examinations: Using the compensation of disadvantage as used in German state examinations in items with partial credit scoring. [PDF]
GMS J Med Educ, 2018 Möltner A.
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, 2014 A credit score is index, which measures the credit risk. It absorbs the information from your credit history into a single number at a particular point of time. This forms a basis for the assessment of your credit report and rating your credit risk by lenders.
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Transforming financial documents into credit decisions using explainable artificial intelligence and optical character recognition. [PDF]
MethodsXMalave S +5 more
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Transformer-based NLP approaches for credit risk prediction: a systematic review. [PDF]
Front Artif IntellRaliphada P, Olukanmi S, Olusanya M.
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