18th -19th July 2019 | Sheraton Imperial Kuala Lumpur

Course Overview
The course will provide attendees with detailed tuition on probability of default credit modelling from empirical, theoretical and applied perspectives. A hands-on perspective is followed by providing a toolkit to be directly used on a day-by-day basis. In this regards, R is open source statistical software used throughout the journey. Indeed, this tool is used by major financial institutions as well as smaller companies due to its flexibility and extreme versatility. Furthermore, examples are illustrated in Excel for easy understanding and to be beneficial for those who use Excel.
During the first day, the key instruments used for modelling are explored. A wide use of the R software characterizes the course from the very beginning. In day one, the emphasis is on developing point-in-time Probability of Default (PD). An extensive interaction with R paves the way for the next day two as well. The focus of the second day is to expand the time horizon to encompass the entire lifetime.

On this, generalized linear models together with survival analysis are investigated for deriving lifetime PD curves. As during the first day, a specific attention is devoted to the so-called low default portfolios. Stress testing as well as scenario analysis accompany the attendees throughout. Case studies are explored in order to culminate the theoretical concepts into working level knowledge. A constant attention to a forward looking perspective is the primary focus of the journey.

Learning Objectives

  • Working-level knowledge of PD scorecard modelling
  • Acquire corresponding hands-on R software development & Excel
  • Working knowledge of lifetime PD modelling based on generalised linear modelling (GLM)
  • Survival analysis based on R implementation capability
  • Detailed understanding of key validation PD scorecard and lifetime statistics and procedures
  • Successful understanding of PD projections and stress under alternative macroeconomic scenarios
  • Develop alternative models for low default portfolios
  • Use new and advanced techniques for improved credit risk modelling.

Training Methodology
Hands-on R & Excel Masterclass with interactive group discussions, comprehensive case studies and sharing of practical experience.
Who Should Attend
Anyone who is involved in building credit risk models or is responsible for monitoring the behavior and performance of credit risk models
» Risk Managers
» Credit Risk Modellers
» Quantitative Analysts
» Statistical Analysts
» Risk Analysts
» Model Development Managers
» Credit Risk Model Validators


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    +603 2721 4361




    3novex Asia

    Menara UOA Bangsar, A-17-10 No. 5, Jalan Bangsar Utama 1, 59000 Wilayah Persekutuan, Wilayah Persekutuan Kuala Lumpur
    Company Registration Number (1305569-P)