手法を比較

	信用スコアリング（スコアカード、WoE/IV）	アルトマンZスコア：企業倒産の予測	ロジスティック回帰
分野	ファイナンス	ファイナンス	研究統計
系統	Regression model	Regression model	Process / pipeline
提唱年	1997	1968	1958
提唱者	Hand & Henley; Thomas, Edelman & Crook	Edward Altman	David Roxbee Cox
種類	Supervised binary classification model	Multiple discriminant analysis scoring model	Method
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.	Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
別名	Credit Scorecard, Application Scoring, Behavioural Scoring, Kredi Skorlama	Altman's Z-Score Model, Multiple Discriminant Analysis Bankruptcy Model, Z-Score Financial Distress Model, Altman Z-Skoru	logit model, binomial logistic regression, LR
関連	3	3	3
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.	The Altman Z-Score is a linear discriminant model developed by Edward I. Altman in 1968 to predict corporate bankruptcy using five accounting-based financial ratios. Derived through multiple discriminant analysis on a matched sample of 66 US manufacturing firms, the model combines liquidity, profitability, leverage, solvency, and activity ratios into a single composite score that classifies firms as financially sound, distressed, or in a grey zone.	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.