手法を比較

	信用スコアリング（スコアカード、WoE/IV）	ロジスティック回帰	XGBoost
分野	ファイナンス	研究統計	機械学習
系統	Regression model	Process / pipeline	Machine learning
提唱年	1997	1958	2016
提唱者	Hand & Henley; Thomas, Edelman & Crook	David Roxbee Cox	Chen, T. & Guestrin, C.
種類	Supervised binary classification model	Method	Ensemble (gradient-boosted decision trees)
原典	Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: a review. Journal of the Royal Statistical Society: Series A, 160(3), 523–541.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
別名	Credit Scorecard, Application Scoring, Behavioural Scoring, Kredi Skorlama	logit model, binomial logistic regression, LR	XGBoost, extreme gradient boosting, scalable tree boosting
関連	3	3	5
概要	Credit scoring is a statistical technique that estimates the probability that a borrower will default on a financial obligation. Using Weight of Evidence (WoE) binning, Information Value (IV) variable selection, and logistic regression, it converts raw applicant data into a single integer score. Formalized by Hand and Henley (1997) and elaborated by Thomas, Edelman, and Crook, the scorecard framework has become the regulatory standard for retail credit risk assessment in banking, lending, and insurance.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.