Enhancing credit scoring accuracy with a comprehensive evaluation of alternative data. [PDF]
PLoS OneHlongwane R, Ramaboa KKKM, Mongwe W.
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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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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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Large unbalanced credit scoring using Lasso-logistic regression ensemble. [PDF]
PLoS One, 2015 Wang H, Xu Q, Zhou L.
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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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Consumer credit scoring: do situational circumstances matter?
Although credit history scoring offers benefits to lenders and borrowers, failure to consider situational circumstances raises important statistical issues that may affect the ability of scoring systems to accurately quantify an individual's credit risk.
Robert B. Avery +2 more
core
AI-Mediated Assessment of Continuing Medical Education: The Case-based Learning Intelligence Credit System (CLICS) Framework. [PDF]
JMIR Med EducWattanasirichaigoon S.
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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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