Developing & Implementing Intelligent Credit Risk Scorecards

Developing & Implementing Intelligent Credit Risk Scorecards

 Sigla_IBR_2017 S285-sas100K NOU 

 

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 key concepts that will be covered are:

  • The application of business intelligence to the scorecard development process, so that the development and implementation of scorecards is seen as an intelligent business solution to a business problem.
  • The concept of building scorecards that contain predictive variables representing major information categories.
  • View of scorecards as decision support tools. Scorecards should be viewed as a tool to be used for better decision making, and should be created with this view

Target group

The target audience consists of people who are involved into building scoring systems and/or are responsible for monitoring their behaviour and performance.

Prerequisite

Credit scoring and an understanding of statistical classification methods is recommended.

Course Outline

A Credit Scoring Overview

  • Credit Scoring Types
  • Areas of Use
  • Development Process

Regulatory Environment

  • Basel III Accord
  • Credit Risk Parameters
  • Targeted Review of Internal Models (TRIM)

Introduction to SAS® Enterprise Miner

  • Solution’s Interface Overview
  • Analysis Element Organization
  • Creating a SAS Enterprise Miner Project

Accessing & Preparing Data for Scorecard Development

  • Defining a Data Source
  • Exploring a Data Source
  • Developmental 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 Development

  •  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

 

Contact

Emilia Frunza

Training Manager

Tel: 0748.886.834; E-mail: emilia.frunza@ibr-rbi.ro