A practical approach to validating a pd model
In this era of evidence-based medicine, randomized clinical trials are the basis for assessment of treatment efficacy.Prediction models are key to individualizing diagnostic and treatment decision-making.The course is, however, also very relevant to senior staff members and to regulators, who need a thorough understanding of the "workings" of the IRB-A approach and of the challenges of estimating, implementing and validating models for quantifying credit risk.We start with a brief introduction to the IRB approach to measuring credit risk.He also led the creation of an end-to-end model development, deployment and monitoring solution and defined specific functionality for building internal rating systems for Basel 2 -PD and LGD modeling, pooling and backtesting.After writing SAS' first Risk Weighted Assets calculation code, he helped launch SAS' market leading Credit Risk Management solution.He became a consultant in 2006 providing credit risk and internal audit departments with advisory and implementation services, such as readiness assessment, model development and rating system auditing.Clients include inter alia GHB bank, Thailand, (Housing Loan Application Scorecard), Samlink, Finland, (Behavioral PD Model), Maybank Malaysia( Corporate PD Model Validation), National Australia Group UK, (Retail PD, LGD and EAD Model Validation for Basel2 IRB Approval), Deutsche Telekom Germany (PD model validation and development, early warning system, profit scoring) and BHW Bausparkasse ( PD and LGD model validation of a home loans portfolio).
The course is designed as mix of theoretical presentations/discussions, practical examples and small exercises.It can be used as a textbook for a graduate course on predictive modeling in diagnosis and prognosis.It is beneficial if readers are familiar with common statistical models in medicine: linear regression, logistic regression, and Cox regression. But it also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling.This course provides practical knowledge for the validation and monitoring of credit scoring and internal rating systems with a focus on the relevant statistical techniques and tools.Attendees should have a background in predictive modelling or credit risk management and want to learn more about validating predictive models in the credit risk area, especially in the context of Basel 2.
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The set forth methodology and tests are the summary of the authors’ statistical expertise and experience of world-wide observed business practices.