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Credit Scorecard Development and Implementation
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Credit Scorecard Development and Implementation
  • Overview
  • Objectives & Outline
  • Methodology
  • Participant Profile
  • Trainer
  • Overview
    PROGRAMME DETAILS

    DATE

    30 – 31 October 2018

    TIME

    9:00 AM – 5:00 PM

    VENUE

    Asian Banking School
    14
    AICB
    CPD HOURS
    This programme provides a high-level introduction to credit risk management; detailed end-to-end methodology training for risk scorecard development for retail and SME portfolios; discussions on scorecard implementation and risk strategy development; and scorecard and portfolio management reporting. It will focus on the development of application scorecards but issues relating to behaviour scorecard development will also be explored. Issues relevant to Basel II will be covered. These objectives are reflected in three sections:

    Section 1: Introduction to Credit Risk: participants will get a high-level overview of the credit risk industry, risk management tools and strategies. Students will understand the different uses of credit risk scorecards and learn industry terminology, as well as understand the main personas involved in successful credit scoring projects.

    Section 2: Risk Scorecard Development: participants will learn how grouped-variable, points-based credit risk scorecards are developed, from planning stage to delivery. While the focus will be on business issues, statistical aspects of scorecard development will also be explored.

    Section 3: Implementation and Maintenance: this section will cover post-development activities including setting cut-offs, strategy development and scorecard maintenance reports.
    LEARNING LEVEL

    Advanced

    PROGRAMME FEE*

    AICB MEMBER

    MYR

    6,000

    / PAX

    NON-MEMBER

    MYR

    6,500

    / PAX

    *inclusive of GST 
  • Objectives & Outline
    LEARNING OBJECTIVES
    By the end of the programme, participants will be able to:
    • Recognize the different uses for credit risk scorecards
    • Recognize the main personas involved in successful credit scoring projects
    • Plan a successful scorecard development project
    • Develop grouped-variable, points-based credit risk scorecards, from start to finish
    • Perform validation, use multiple scorecards, set cut-offs
    • Develop strategies using scorecards
    • Create reports on scorecard validation and maintenance
    PROGRAMME OUTLINE
    Introduction to Credit Risk
    • Risk management areas
    • Terminology
    • Application of credit scoring in various industries 

    Preliminaries and Planning
    • Create a business plan: identify organisational objectives, and internal vs. external development, and scorecard type
    • Create project plan: identify project risks and identify project team

    Data Review and Project Parameters
    • Data availability and quality review
    • Data gathering for definition of project parameters
    • Definition of project parameters: performance window and sample window, performance categories definition and exclusions
    • Segmentation

    Development Database Creation
    • Development sample specifications
    • Development data collection and construction
    • Adjusting for prior probabilities/factoring

    Model Development
    • Missing values and outliers
    • Initial characteristic analysis (binning): statistical measures and logical trends
    • Building a preliminary scorecard using logistic regression
    • Reject inference
    • Final scorecard production: scaling, misclassification and scorecard strength measures
    • Validation

    Scorecard Management Reports
    • Gains tables
    • Characteristic reports

    Pre-Implementation
    • Pre-implementation validation: scoring accuracy and front-end validation

    Strategy Development
    • Strategy development considerations
    • Scoring strategies
    • Setting cut-offs
    • Strategy actions
    • Policy rules
    • Overrides

    Post-Implementation
    • Review/post-mortem
    • Reporting: system stability, characteristic, override, final score, delinquency, and vintage
  • Methodology

    Interactive lectures in a classroom style

  • Participant Profile
    Credit risk/scoring managers and data miners, those involved in model vetting/validation and auditing, risk strategy developers and credit risk executives. No SAS experience or programming experience is required. Participants should be familiar with logistics regression. Knowledge of SAS Enterprise Miner is helpful, but not necessary.
  • Trainer

    Naeem Siddiqi

    Naeem Siddiqi, Director, Credit Scoring and Decisioning in the Risk and Quantitative Solutions Division at SAS, and author of Intelligent Credit Scoring: Building and Implementing Credit Risk Scorecards

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