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SAS(R) Expected Credit Loss: Solution Overview for IFRS9

Course Details
Code: RECL2
Tuition (USD): $975.00 • Classroom (1.5 days)

The solution overview training for SAS Expected Credit Loss prepares members of your organization's project team to be effective and informed participants in the requirements, development, and solution-design phases of your implementation. This course includes hands-on demonstrations and teaches you key concepts, terminology, and base functionality that is integral to the SAS Expected Credit Loss solution. After completing the course, participants will be prepared to make key business decisions.

Skills Gained

  • describe the role and use of each component of the SAS Expected Credit Loss solution
  • describe the process of calculating expected credit loss
  • describe the required input data sets for each component
  • leverage SAS Business Rules Manager to publish rule flows in support of data quality and stage allocation
  • use SAS Model Implementation Platform to create the required objects that are needed to generate Expected Credit Loss output data sets
  • leverage SAS Risk and Finance Workbench to orchestrate the monthly production cycle, including the configuration of the required objects

Who Can Benefit

  • Anyone who is involved in implementing and using the SAS Expected Credit Loss solution, including members from the Finance or Accounting, Risk Management, and Model Execution or Deployment Departments, as well as solution administrators and IT associates

Prerequisites

  • There are no prerequisites for this course.

Course Details

Introduction to SAS Expected Credit Loss

  • defining IFRS9
  • exploring components of SAS Expected Credit Loss
  • IFRS9 monthly production workflow example

Examining Data Quality and Stage Allocation Rules

  • introduction to SAS Business Rules Manager
  • working with input data and a vocabulary
  • working with rule flows
  • testing and publishing a rule flow

Implementing Models Using SAS Model Implementation Platform

  • introduction to the Model Implementation Platform
  • working with input data sets
  • creating portfolio analysis objects
  • analyzing a portfolio
  • using High-Performance Risk Explorer to analyze portfolio results
  • publishing a scenario run to a modeling system

Managing the Expected Credit Loss Process

  • introduction to SAS Risk and Finance Workbench
  • preparing the environment
  • reviewing the results of environment preparation
  • working with the Expected Credit Loss data
  • reviewing data within the user interface
  • using the worksheets in the Expected Credit Loss solution
  • using cell-level detail
  • generating report output
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