Vertaile menetelmiä

	Luottoluokitus (Scorecards, WoE/IV)	XGBoost
Tieteenala	Rahoitus	Koneoppiminen
Menetelmäperhe	Regression model	Machine learning
Syntyvuosi	1997	2016
Kehittäjä	Hand & Henley; Thomas, Edelman & Crook	Chen, T. & Guestrin, C.
Tyyppi	Supervised binary classification model	Ensemble (gradient-boosted decision trees)
Alkuperäislähde	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.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Rinnakkaisnimet	Credit Scorecard, Application Scoring, Behavioural Scoring, Kredi Skorlama	XGBoost, extreme gradient boosting, scalable tree boosting
Liittyvät	3	5
Tiivistelmä	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.	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.