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SAS Credit Risk Modelling- LGD/EAD & Scorecards
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- FormatePub
- ISBN8231420889
- EAN9798231420889
- Date de parution25/08/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurWalzone Press
Résumé
Build bank-grade LGD and EAD models end to end-using SAS. This hands-on guide shows how to go from raw banking data to production-ready scorecards, with every step demonstrated in executable SAS code and explained in plain language. What's inside Data design for LGD/EAD: default events, recovery cashflows (PV), exposure panels, keys, and time windows. LGD mechanics: constructing recovery vectors, discounting to present value, bounded/quantile modelling, and calibration.
EAD approaches: revolving CCF (beta GLM or two-part draw/size) and amortizing EAD with Tweedie/log-link. Validation on future data: DEV vs OOT splits, MAE/RMSE, calibration-by-decile, and stability/PSI. Downturn overlays: straightforward ratio method plus macro-linked options for policy and IFRS-9 alignment. Scorecards & deployment: scaling, reporting, monthly scoring outputs, and governance checklists.
Why it's practical SAS-first workflows (Base/Macro, PROC SQL, LOGISTIC, GLIMMIX, GENMOD) you can adapt immediately. Synthetic datasets that mirror real banking structures, so examples are safe and reproducible. Clear documentation patterns that satisfy validation and audit. Who should read thisRisk analysts, SAS developers, model validators, and product owners who need LGD/EAD models that are explainable, stable, and ready for production-without wading through academic theory.
By the end, you'll have a complete pipeline for LGD, EAD, and scorecards: data ? features ? models ? validation ? monitoring ? deployment.
EAD approaches: revolving CCF (beta GLM or two-part draw/size) and amortizing EAD with Tweedie/log-link. Validation on future data: DEV vs OOT splits, MAE/RMSE, calibration-by-decile, and stability/PSI. Downturn overlays: straightforward ratio method plus macro-linked options for policy and IFRS-9 alignment. Scorecards & deployment: scaling, reporting, monthly scoring outputs, and governance checklists.
Why it's practical SAS-first workflows (Base/Macro, PROC SQL, LOGISTIC, GLIMMIX, GENMOD) you can adapt immediately. Synthetic datasets that mirror real banking structures, so examples are safe and reproducible. Clear documentation patterns that satisfy validation and audit. Who should read thisRisk analysts, SAS developers, model validators, and product owners who need LGD/EAD models that are explainable, stable, and ready for production-without wading through academic theory.
By the end, you'll have a complete pipeline for LGD, EAD, and scorecards: data ? features ? models ? validation ? monitoring ? deployment.
Build bank-grade LGD and EAD models end to end-using SAS. This hands-on guide shows how to go from raw banking data to production-ready scorecards, with every step demonstrated in executable SAS code and explained in plain language. What's inside Data design for LGD/EAD: default events, recovery cashflows (PV), exposure panels, keys, and time windows. LGD mechanics: constructing recovery vectors, discounting to present value, bounded/quantile modelling, and calibration.
EAD approaches: revolving CCF (beta GLM or two-part draw/size) and amortizing EAD with Tweedie/log-link. Validation on future data: DEV vs OOT splits, MAE/RMSE, calibration-by-decile, and stability/PSI. Downturn overlays: straightforward ratio method plus macro-linked options for policy and IFRS-9 alignment. Scorecards & deployment: scaling, reporting, monthly scoring outputs, and governance checklists.
Why it's practical SAS-first workflows (Base/Macro, PROC SQL, LOGISTIC, GLIMMIX, GENMOD) you can adapt immediately. Synthetic datasets that mirror real banking structures, so examples are safe and reproducible. Clear documentation patterns that satisfy validation and audit. Who should read thisRisk analysts, SAS developers, model validators, and product owners who need LGD/EAD models that are explainable, stable, and ready for production-without wading through academic theory.
By the end, you'll have a complete pipeline for LGD, EAD, and scorecards: data ? features ? models ? validation ? monitoring ? deployment.
EAD approaches: revolving CCF (beta GLM or two-part draw/size) and amortizing EAD with Tweedie/log-link. Validation on future data: DEV vs OOT splits, MAE/RMSE, calibration-by-decile, and stability/PSI. Downturn overlays: straightforward ratio method plus macro-linked options for policy and IFRS-9 alignment. Scorecards & deployment: scaling, reporting, monthly scoring outputs, and governance checklists.
Why it's practical SAS-first workflows (Base/Macro, PROC SQL, LOGISTIC, GLIMMIX, GENMOD) you can adapt immediately. Synthetic datasets that mirror real banking structures, so examples are safe and reproducible. Clear documentation patterns that satisfy validation and audit. Who should read thisRisk analysts, SAS developers, model validators, and product owners who need LGD/EAD models that are explainable, stable, and ready for production-without wading through academic theory.
By the end, you'll have a complete pipeline for LGD, EAD, and scorecards: data ? features ? models ? validation ? monitoring ? deployment.
