השוואת שיטות

	מערכת הדירוג CAMELS	ציון Z של אלטמן: חיזוי פשיטת רגל של חברות	דירוג אשראי (כרטיסי ניקוד, WoE/IV)
תחום	מימון	מימון	מימון
משפחה	Process / pipeline	Regression model	Regression model
שנת המקור	1998	1968	1997
הוגה השיטה	US bank supervisory framework; Cole & Gunther	Edward Altman	Hand & Henley; Thomas, Edelman & Crook
סוג	Composite supervisory rating	Multiple discriminant analysis scoring model	Supervised binary classification model
מקור מכונן	Cole, R. A., & Gunther, J. W. (1998). Predicting bank failures: A comparison of on- and off-site monitoring systems. Journal of Financial Services Research, 13(2), 103–117.	Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.	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.
כינויים	CAMELS Framework, Uniform Financial Institutions Rating System, UFIRS, CAMELS Derecelendirme Sistemi	Altman's Z-Score Model, Multiple Discriminant Analysis Bankruptcy Model, Z-Score Financial Distress Model, Altman Z-Skoru	Credit Scorecard, Application Scoring, Behavioural Scoring, Kredi Skorlama
קשורות	3	3	3
תקציר	The CAMELS Rating System is a supervisory framework used by US bank regulators to evaluate the overall condition of financial institutions across six dimensions: Capital Adequacy, Asset Quality, Management, Earnings, Liquidity, and Sensitivity to Market Risk. Each component is scored on a scale of 1 (strong) to 5 (critically deficient), and a composite score is assigned based on examiner judgment. Developed in the US federal banking regulatory context, CAMELS emerged as the standard on-site examination tool and has since been adopted and adapted by regulators globally.	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.	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.