Sameer Shaikh

SAS Stress Testing, IFRS 9 & Capital Forecasting (eBook, ePUB)

• Format: ePub

Produktbeschreibung

SAS Stress Testing, IFRS 9 & Capital Forecasting is a practitioner's playbook for turning PD/LGD/EAD models into board-ready capital views. The book walks from a reproducible SAS environment and a realistic 50k synthetic portfolio through loss estimation, stress overlays, IRB-style capital (K, UL, RWA), and full ICAAP/CCAR projections-so readers can go from "model metrics" to decisions on limits, buffers, and pricing.

What makes this book different:

· Guaranteed-run code that is idempotent and safe to re-run (writes to a clean WORK library), plus copy-paste exhibits you can lift straight into documentation and decks.

· Business-first outputs: Board/ALCO tables for EL & RWA by segment, IRB capital rollups, and one-page pricing floors linking EL rate and capital charge.

· Forward-looking stress testing: Mild/Moderate/Severe scenarios, a 2008-style case study, and reverse-stress breakpoints that connect macro shocks to PD, LGD, and EAD.

· End-to-end governance: How to translate analytics into ICAAP narratives, CCAR-style quarter rolls, and audit-ready evidence with clean SAS listings.

Who this is for: risk modellers and validation teams, capital planners, IFRS 9/CECL practitioners, and managers who want transparent, reproducible SAS code that produces numbers boards and regulators trust.

How to use this book: start with the environment header and portfolio generator (Part 1), run the profiling snapshots, then build the EL/Capital exhibits before moving into scenario overlays and ICAAP/CCAR projections. Each chapter ends with copy-ready tables and explanations you can paste into your bank's documentation.

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Produktdetails

• Verlag: Sameer Shaikh
• Erscheinungstermin: 25. August 2025
• Englisch
• ISBN-13: 9798231617265
• Artikelnr.: 75282175

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.