Methoden vergleichen

	Kredit-Scoring (Scorecards, WoE/IV)	Altman Z-Score: Vorhersage von Unternehmensinsolvenzen	Logistische Regression	XGBoost
Fachgebiet	Finanzwirtschaft	Finanzwirtschaft	Forschungsstatistik	Maschinelles Lernen
Familie	Regression model	Regression model	Process / pipeline	Machine learning
Entstehungsjahr	1997	1968	1958	2016
Urheber	Hand & Henley; Thomas, Edelman & Crook	Edward Altman	David Roxbee Cox	Chen, T. & Guestrin, C.
Typ	Supervised binary classification model	Multiple discriminant analysis scoring model	Method	Ensemble (gradient-boosted decision trees)
Wegweisende Quelle	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.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Aliasnamen	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	XGBoost, extreme gradient boosting, scalable tree boosting
Verwandt	3	3	3	5
Zusammenfassung	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.	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.