Managing risks and improving profits in a world of uncertainties
20-21 September 2023 | Bangkok, Thailand
Retail lending has changed dramatically in the last couple of decades. Machine Learning and AI bring new capabilities, regulations like IFRS 9 require model enhancements, and all models receive much greater audit scrutiny. Reliance on weak, out-dated models can create portfolio disasters. Managing a retail loan portfolio successfully depends on properly developing, validating, deploying, and integrating a wide range of models and new technologies.
This course is for analysts, managers, and credit professionals who are involved in model development, validation, or model risk management.
Although the course covers the development and use of sophisticated analytical techniques, it is intended for portfolio managers, financial analysts, credit policy professionals, and statistical analysts. Anyone with decisionmaking responsibility in this field will benefit. Discussions will minimize mathematical derivations, instead emphasizing intuitive understanding, use of available algorithms, and best practices in application and implementation.
- Learn the fundamentals of retail lending analytics.
- Gain an understanding for when these methods can be used reliably, when they fail, and how to use new methods to succeed.
- Obtain a deep understanding of the drivers of credit risk, and how various models capture some, or all, of these drivers.
- Learn how to leverage the latest techniques in machine learning with an eye to successful validation and regulatory examination.
- Learn to develop accurate credit risk models that get into production quickly and can be readily updated to support accurate lending decisions. Shorter time to value yields significant ROI.
- Acquire best practices for building models for IFRS 9, stress testing, profitability, and model risk management
SAS Training Center, SAS Software (Thailand) Co., Ltd.
388 Exchange Tower Building, 38 Fl., Unit 3803-4, Sukhumvit Road, Klongtoey
Bangkok 10110 Thailand
Joseph L. Breeden Ph.D., Chief Executive Officer, Deep Future Analytics LLC,
www.deepfutureanalytics.com, President of MRMIA
Dr. Breeden has 25 years of experience in financial services and is a recognized leader in the industry. At DFA, he is directly involved in research into new modeling techniques and products. He co-founded Strategic Analytics in 1999, where he led the design of advanced analytic solutions including the invention of Dual-time Dynamics.
Dr. Breeden has created models through the 1995 Mexican peso crisis, the 1997 Asian economic crisis, the 2001 global recession, the 2003 Hong Kong SARS recession, and the 2007–2009 U.S. mortgage crisis and global financial crisis. These crises have provided Dr. Breeden with a rare perspective on crisis management and the analytics needs of executives for strategic decision-making. Dr. Breeden’s own models were successful throughout the U.S. mortgage crisis and warned of problems as early as the beginning of 2006.
His book Reinventing Retail Lending Analytics: Forecasting, Stress Testing, Capital, and Scoring for a World of Crises was published by Riskbooks in 2010. He currently serves as associate editor for the Journal of Credit Risk and the Journal of Risk Model Validation.
You should be comfortable with the basics of retail lending in at least one area and have some familiarity with building models in banking and have a basic understanding of credit scoring.
For questions about this course, please contact Dr. Joseph Breeden, firstname.lastname@example.org
Day 1 (9:00 a.m. – 5:00 p.m.)
Day 2 (9:00 a.m. – 5:00 p.m.)
Model Types Overview
Collective vs Individual Assessment
Comparison of Strengths and Weaknesses
Quantifying Adverse Selection
Multihorizon Survival Models
SAS Intelligent Decision Engine
Credit Decisioning Life Cycle to Modern credit risk modeling and decision making
Model Risk Management
Stages of impairment
12-month vs lifetime expected credit losses
Techniques and tips
Issues by asset class
Regulatory Capital (Basel II)
Machine Learning Best Practices
Which methods work where, and why
ML failure risks
ML compliance risks
Global Solution Lead – Risk Modeling and Decisioning
Dr Terisa Roberts is the global solution lead for Risk Modeling and Decisioning at SAS. She has nearly two decades of experience in quantitative risk management and advanced analytics. Throughout her career, she has helped companies in financial services, telecommunications, government, energy and retail derive business value and make better decisions using risk analytics. She has domain experience in regulatory compliance, IFRS9, BCBS239, model risk management and enterprise stress testing. She advises banks and regulators around the world on industry best practices in Artificial Intelligence, automation and digitalization related to risk modeling and decisioning and in the responsible use of AI and Machine Learning. She regularly speaks at international Risk conferences on innovation in Risk Management. She holds a Ph. D in Operations Research and Informatics and lives in Sydney, Australia with her family.
US $750 per attendee for the course, lunch and snacks included both days.