Developing & Implementing Intelligent Credit Risk Scorecards
Hands-on 2-day seminar with SAS software
Aims and Scope of the Seminar
This seminar presents a business-focused process for the development and implementation of risk prediction scorecards, one that builds upon a solid foundation of statistics and data mining principles.
Moreover, this course aims to define the concepts underpinning credit risk modelling and to show how these concepts can be formulated with practical examples using SAS software. Through the specific course students learn how to develop credit risk models in the context of the Basel Guidelines. These are illustrated by structured course notes and exercises and by several real-life cases. It will be highly interactively organised.
The target audience consists of analysts and technical managers who are involved into building scoring systems and/or are responsible for monitoring their behaviour and performance, or wishing to build credit risk or marketing oriented scorecards.
Understanding of statistical classification methods and credit risk principles will be welcome.
A Credit Scoring Overview
- Credit Scoring Types
- Areas of Use (Not Only Credit Risk)
- Development Process
Accessing & Preparing Data for Scorecard Development
- Defining a Data Source
- Exploring a Data Source
- Training and Validation Data Sets
How to Interactively Group Data
- Group the Strongest Characteristics
- Metrics for the Analysis of Characteristics
- Improve the Predictive Power of the Characteristic
- Scorecard Node in SAS® Enterprise Miner™
- Logistic Regression
- Scorecard Management Reports
Comparison of Different Models
- Model Comparison Node in SAS® Enterprise Miner™
- Classification Measures
- Statistical Measures
- Data Mining Measures
Introduction to SAS® Credit Scoring
- Solution’s Components Overview
- Analytical Data Set Builder
- Model Specification and Versioning
- Backtesting and Deployment
- Probability of Default and Other Credit Risk Parameters (according to CRD/ Basel Accord)
- Targeted Review of Internal Models (TRIM)
Model Risk Management
- Model Risk Management Lifecycle Overview
- Model Validation and Ongoing Monitoring
- Change Management
It is not Only About Credit Risk
- Alternative Uses od Scorecards
- Advanced Modeling and Machine Learning Capability
Przemyslaw Janicki, PRM – SAS Senior Industry Consultant – Risk Practice South EMEA
Przemyslaw is an expert in the area of Basel II/III/IVCRD (with an experience of over 12 years), in credit risk management and economic risk methodology. He has over 10 years of experience in ALM and a solid knowledge of financial products and hedging strategies (mainly fixed income securities and derivatives, including exotic options).
Throughout his career he has held seminars for banking professionals, on issues related to the implementation of ALM calculation and reporting system (LCR/NSFR/ALMM) – at a global bank headquartered in London; implementation of Basel III/CRD IV regulations in the area of credit and market risk – various banks in Germany, France, the Netherlands and Poland; development of credit scoring and rating systems – top5 banks in Poland; implementation of the system for credit limits establishment, valuation of financial instruments and market risk management – the Polish National Bank; implementation of Stress Testing mechanism – top 3 banks in Poland; implementation of SAS IFRS9 Solution – top3 banks in Poland; implementation of IFRR/FTP tool (including IRRBB) – top 3 banks in Poland.
27-28 March, 2019, between 9:00-17:00. Registration deadline: 18 March
Tel: 0748.886.807; E-mail: firstname.lastname@example.org