Sameer Shaikh

SAS Credit Risk Modelling- LGD/EAD & Scorecards (eBook, ePUB)

• Format: ePub

Produktbeschreibung

Build bank-grade LGD and EAD models end to endusing 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 this Risk analysts, SAS developers, model validators, and product owners who need LGD/EAD models that are explainable, stable, and ready for productionwithout wading through academic theory.

By the end, you'll have a complete pipeline for LGD, EAD, and scorecards: data → features → models → validation → monitoring → deployment.

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Produktdetails

• Verlag: Sameer Shaikh
• Erscheinungstermin: 25. August 2025
• Englisch
• ISBN-13: 9798231420889
• Artikelnr.: 75282529

Autorenporträt

Sameer Shaikh is a Senior Data Architect with over 14 years' experience in Banking, fraud detection, and credit-risk modeling for leading banks in India and United Arab Emirates. A recognized thought-leader, Sameer has:

• Architected enterprise AML/Credit Risk platforms in SAS Viya and SAS AML Manager, delivering real-time solutions
• Built advanced machine-learning models (logistic regression, gradient boosting, random forests) in SAS Enterprise Miner and SAS Studio, achieving up to 95% detection rates on synthetic fraud scenarios.
• Pioneered network analytics by integrating Gephi visualizations with SAS data flowsuncovering hidden rings of mule accounts and circular money-movement patterns.
• Automated regulatory reporting in private banks in India, Singapore and Malaysia
• Mentored dozens of junior analysts through internal "SAS training programs, fostering a new generation of Tech specialists.