

ECL Expected Credit Loss Operation
Overview:
Introduction:
ECL (Expected Credit Loss) operation refers to the processes and methodologies used by financial institutions to estimate and manage credit loss in compliance with IFRS 9 standards. It focuses on predicting potential credit losses over the lifecycle of financial assets to ensure effective risk management and regulatory adherence. This training program equips participants with the knowledge and tools to implement and manage ECL operations effectively, aligning with organizational and compliance objectives.
Program Objectives:
By the end of this program, participants will be able to:
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Identify the principles and significance of ECL under IFRS 9.
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Develop models for estimating expected credit loss.
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Manage and integrate ECL processes within financial systems.
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Analyze and report ECL metrics for decision-making and compliance.
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Address operational challenges and enhance ECL frameworks.
Targeted Audience:
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Credit risk analysts and managers.
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Financial reporting professionals.
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Compliance officers and auditors.
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Risk management specialists in financial institutions.
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Professionals involved in credit loss estimation and management.
Program Outline:
Unit 1:
Introduction to Expected Credit Loss (ECL):
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Overview of IFRS 9 and the concept of ECL.
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Differences between incurred loss and expected credit loss models.
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Key components of ECL: probability of default (PD), loss given default (LGD), and exposure at default (EAD).
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Significance of ECL in credit risk management and financial reporting.
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Challenges in implementing ECL frameworks in financial institutions.
Unit 2:
Developing ECL Models:
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Steps for building robust ECL models.
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Data requirements for ECL estimation and modeling.
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Techniques for calculating PD, LGD, and EAD.
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Approaches for staging financial assets under IFRS 9.
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Validating and stress-testing ECL models.
Unit 3:
Operationalizing ECL Processes:
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The process of integrating ECL models into financial systems and workflows.
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Automating ECL calculations for efficiency and accuracy.
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Ensuring data quality and consistency in ECL operations.
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How to manage multi-scenario analysis for ECL forecasting.
Unit 4:
ECL Reporting and Compliance:
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Requirements for ECL disclosure under IFRS 9.
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Frameworks for generating ECL reports for regulatory and internal stakeholders.
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How to analyze ECL data to support decision-making.
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Addressing compliance challenges in ECL operations.
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Adapting ECL frameworks to meet evolving regulatory standards.
Unit 5:
Enhancing ECL Frameworks:
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Importance of incorporating advanced analytics and machine learning in ECL.
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Key activities for leveraging historical data to improve ECL accuracy.
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Addressing operational risks in ECL processes.
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Continuous improvement strategies for ECL frameworks.