Porównaj metody

	Scoring kredytowy (tabele punktowe, WoE/IV)	Altman Z-Score: Prognozowanie bankructwa przedsiębiorstwa	XGBoost
Dziedzina	Finanse	Finanse	Uczenie maszynowe
Rodzina	Regression model	Regression model	Machine learning
Rok powstania	1997	1968	2016
Twórca	Hand & Henley; Thomas, Edelman & Crook	Edward Altman	Chen, T. & Guestrin, C.
Typ	Supervised binary classification model	Multiple discriminant analysis scoring model	Ensemble (gradient-boosted decision trees)
Źródło pierwotne	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.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Inne nazwy	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	XGBoost, extreme gradient boosting, scalable tree boosting
Pokrewne	3	3	5
Podsumowanie	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.	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.