Implementation Guide

IFRS 9 Implementation Step by Step Guide

Complete professional guide to implementing IFRS 9 Expected Credit Loss model. From planning to go-live, this comprehensive roadmap covers data requirements, modeling approaches, system implementation, and regulatory compliance.

18-36 Month Timeline6 Implementation PhasesRegulatory Compliant15,000+ Words

Table of Contents

Navigate through the complete IFRS 9 implementation process

1
Planning and Scoping
2
Data Assessment and Collection
3
Model Development and Validation
4
System Implementation
5
Testing and Parallel Run
6
Go-Live and Monitoring
Phase 1

Planning and Scoping

Establish governance, assess impact, and develop comprehensive implementation strategy

Governance Structure

Executive Steering Committee
Senior leadership oversight and decision-making
Project Management Office
Dedicated project management and coordination
Working Groups
Cross-functional teams for specific workstreams

Impact Assessment

Financial Impact Analysis
Quantify P&L and balance sheet impacts
Systems Assessment
Identify required technology changes
Resource Planning
Staffing requirements and budget allocation

Key Deliverables

Documentation

  • • Project charter and scope statement
  • • Implementation roadmap and timeline
  • • Risk assessment and mitigation plan
  • • Communication and training plan

Approvals

  • • Executive sponsorship and funding
  • • Regulatory approval for approach
  • • Board and audit committee endorsement
  • • Budget allocation and resource approval
Phase 2

Data Assessment and Collection

Comprehensive data inventory, quality assessment, and collection strategy development

Historical Loss Data

Minimum 5 years of loss history
Exposure-level granularity
Write-offs and recoveries
Vintage analysis capability

Macroeconomic Data

GDP growth rates
Unemployment rates
Interest rates
Property price indices

Exposure Data

Current outstanding balances
Credit risk ratings/grades
Collateral and guarantees
Contractual terms and conditions

Data Quality Framework

Quality Assessment

Completeness
Missing data identification and remediation
Accuracy
Data validation and error checking
Consistency
Cross-system data reconciliation

Data Architecture

Data Lake/Warehouse
Centralized data storage and processing
ETL Processes
Automated data extraction and transformation
Data Governance
Data ownership and quality controls
Phase 3

Model Development and Validation

Build and validate PD, LGD, and EAD models with comprehensive testing and documentation

Probability of Default (PD) Models

Scorecard Development
Application and behavioral scoring models
Vintage Analysis
Performance analysis by origination period
Forward-Looking Information
Macroeconomic overlays and scenario analysis

Loss Given Default (LGD) Models

Workout LGD
Recovery rates from distressed sales
Market LGD
Current market value-based calculations
Collateral Valuation
Real estate and asset appraisal models

Model Validation Framework

Quantitative Validation

  • • Discriminatory power (AUC, Gini)
  • • Calibration accuracy
  • • Back-testing performance
  • • Out-of-sample testing

Qualitative Validation

  • • Model documentation review
  • • Code and logic verification
  • • Data quality assessment
  • • Assumptions and limitations

Ongoing Monitoring

  • • Performance tracking reports
  • • Model stability metrics
  • • Annual validation reviews
  • • Change management procedures

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