Course Introduction
The Expected Credit Loss Modelling and Audit Training Course provides a practical and structured approach to understanding, developing, evaluating, and auditing expected credit loss models used to estimate credit impairment. The course is designed for auditors, accountants, credit-risk professionals, finance teams, model specialists, and assurance practitioners who need to understand the connection between credit-risk information, impairment estimates, financial reporting, and audit evidence.
The program examines the complete expected credit loss modelling lifecycle, from portfolio segmentation and data preparation through probability of default, loss given default, exposure at default, discounting, staging, forward-looking information, scenario weighting, model outputs, overlays, and financial statement reporting. Participants will understand how assumptions and methodological choices influence impairment estimates and how auditors can evaluate whether models and outputs are appropriately supported.
A central focus is placed on the quality and integrity of data used in ECL models. Participants will learn how to assess historical default information, borrower characteristics, exposure data, collateral information, repayment behavior, recovery information, credit-risk grades, delinquency indicators, and economic variables. They will develop procedures for testing completeness, accuracy, consistency, lineage, transformations, and interfaces between loan systems, risk platforms, data warehouses, and accounting systems.
The course provides detailed coverage of staging and significant increases in credit risk, including the indicators and evidence used to determine changes in credit risk. Participants will explore twelve-month and lifetime expected credit loss concepts, default definitions, cure rates, historical loss experience, recovery assumptions, probability-weighted scenarios, forward-looking forecasts, and management overlays, together with practical approaches for challenging significant judgments and estimation uncertainty.
Model validation and audit procedures receive substantial attention throughout the program. Participants will examine back-testing, benchmarking, sensitivity analysis, outcome analysis, model performance, segmentation, calibration, model changes, data limitations, expert judgment, and governance controls. The course emphasizes that a sophisticated model does not automatically produce reliable results and that auditors need to evaluate model design, implementation, inputs, outputs, controls, and supporting evidence.
Through practical case studies and modelling exercises, participants will analyze credit portfolios, assess ECL calculations, test model inputs, evaluate staging decisions, challenge economic scenarios, investigate overlays, perform independent calculations, and assess financial reporting implications. The course also addresses automation, AI-supported credit modelling, cybersecurity, model governance, continuous monitoring, and emerging sources of model risk in modern financial institutions.
Duration
5 days
Who Should Attend
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External auditors auditing expected credit loss estimates and impairment balances.
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Internal auditors reviewing credit-risk, impairment, model governance, and financial reporting controls.
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Audit managers and supervisors responsible for reviewing ECL audit procedures.
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Accountants responsible for impairment calculations and financial instrument reporting.
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Finance managers overseeing expected credit loss processes and financial reporting.
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Credit-risk professionals involved in ECL modelling, portfolio monitoring, and impairment assessment.
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Credit analysts working with borrower risk, default, recovery, and portfolio information.
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Model risk professionals responsible for model development, validation, and monitoring.
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Financial reporting specialists working with financial instrument impairment.
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Banking and financial services professionals responsible for credit portfolio analysis.
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Data analysts supporting credit-risk models and financial impairment calculations.
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Valuation and quantitative professionals involved in financial risk modelling.
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Internal control specialists evaluating controls around ECL models and data.
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Forensic auditors investigating unusual impairment movements or potential financial manipulation.
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Professionals seeking practical expertise in ECL model audit, validation, and financial reporting.
Course Objectives
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Develop practical expertise in expected credit loss modelling principles, methodologies, assumptions, implementation, validation, and audit procedures.
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Understand the relationship between credit risk, impairment measurement, financial reporting, model outputs, and audit assertions.
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Evaluate portfolio segmentation, default definitions, probability of default, loss given default, exposure at default, discounting, and other ECL model components.
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Assess twelve-month and lifetime expected credit loss calculations and evaluate the evidence supporting significant increases in credit risk and staging decisions.
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Test the completeness, accuracy, reliability, consistency, and lineage of borrower, exposure, collateral, repayment, default, recovery, and economic data used in ECL models.
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Evaluate forward-looking information, economic scenarios, probability weighting, forecasts, assumptions, overlays, and other judgments affecting expected credit loss estimates.
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Apply model validation techniques including back-testing, benchmarking, sensitivity analysis, outcome analysis, recalculation, and performance assessment.
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Identify model risk, data-quality weaknesses, implementation errors, inappropriate assumptions, estimation uncertainty, and control deficiencies affecting ECL results.
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Assess technology-enabled ECL environments, automated calculations, system interfaces, AI-supported models, cybersecurity, access controls, and model governance risks.
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Prepare comprehensive audit documentation, findings, recommendations, and conclusions addressing ECL model design, inputs, outputs, controls, financial reporting, and disclosures.
Comprehensive Course Outline
Module 1: Expected Credit Loss Modelling and Audit Fundamentals
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Understanding the purpose of expected credit loss models and their role in credit impairment measurement, financial reporting, risk management, and audit.
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Examining the major components of ECL estimation and how methodological choices influence reported impairment balances and financial performance.
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Linking ECL modelling risks to audit assertions, materiality, estimation uncertainty, management judgment, financial reporting risk, and audit evidence.
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Developing a risk-based ECL audit strategy that considers portfolio significance, model complexity, data quality, estimation uncertainty, and control reliance.
Module 2: ECL Portfolio Segmentation and Credit-Risk Frameworks
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Developing appropriate portfolio segments based on borrower characteristics, product types, risk profiles, collateral, geography, maturity, and other relevant credit-risk factors.
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Evaluating whether portfolio segmentation appropriately reflects differences in credit behavior and expected loss characteristics.
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Assessing credit-risk grades, internal ratings, delinquency indicators, borrower classifications, and other information used to determine impairment and risk migration.
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Auditing segmentation controls and identifying inappropriate grouping, excessive aggregation, insufficient segmentation, or inconsistent treatment of similar exposures.
Module 3: Probability of Default Modelling and Audit
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Understanding probability of default methodologies and assessing historical default information, observation periods, definitions, and data requirements.
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Evaluating point-in-time and through-the-cycle considerations and assessing how credit-risk conditions are reflected in default estimates.
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Testing probability of default calculations, model inputs, calibration procedures, segmentation, assumptions, and adjustments applied by management.
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Applying back-testing and independent analysis to assess whether estimated default probabilities are supported by historical outcomes and relevant evidence.
Module 4: Loss Given Default and Recovery Modelling
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Understanding loss given default methodologies and evaluating the relationship between default, recoveries, collateral, costs, timing, and expected credit losses.
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Testing historical recovery data, collateral values, realization periods, recovery costs, cure rates, and other assumptions incorporated into LGD estimates.
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Assessing whether recovery assumptions appropriately reflect current conditions and reasonable expectations about future recoveries.
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Performing independent recalculations, sensitivity analysis, benchmarking, and outcome comparisons to identify potentially unsupported or overly optimistic recovery assumptions.
Module 5: Exposure at Default and Discounting
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Evaluating exposure at default calculations for loans, commitments, guarantees, revolving facilities, and other credit exposures subject to expected loss assessment.
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Testing outstanding principal, accrued interest, undrawn commitments, credit conversion assumptions, contractual terms, and other EAD model inputs.
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Assessing the treatment of expected future drawdowns and other behavioral assumptions affecting exposure estimates.
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Auditing discounting methodologies and testing cash-flow timing, effective interest rates, contractual maturity assumptions, and calculation accuracy.
Module 6: Staging and Significant Increase in Credit Risk
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Understanding the principles underlying staging decisions and the assessment of significant increases in credit risk over the relevant measurement period.
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Evaluating quantitative and qualitative indicators including delinquency, credit ratings, risk grades, restructuring, watch-list status, borrower conditions, and other credit-risk information.
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Testing staging decisions against source records, credit files, system data, risk classifications, and documented management policies.
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Identifying inappropriate stage migration, delayed recognition of deterioration, inconsistent application of criteria, and other weaknesses affecting lifetime ECL calculations.
Module 7: Forward-Looking Information, Scenarios, and Management Overlays
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Evaluating economic forecasts, macroeconomic variables, scenario design, scenario weighting, and other forward-looking information used in ECL models.
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Assessing whether economic assumptions are appropriately connected to credit-risk outcomes and whether forecast horizons and reversion approaches are adequately supported.
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Testing management overlays and post-model adjustments for methodology, evidence, consistency, documentation, governance, and avoidance of inappropriate bias.
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Applying sensitivity analysis and alternative scenarios to understand estimation uncertainty and assess the potential effect of significant assumptions on reported ECL.
Module 8: ECL Model Validation, Data Analytics, and Technology
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Applying model validation procedures including back-testing, benchmarking, sensitivity analysis, outcome testing, stability analysis, and assessment of predictive performance.
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Using data analytics to identify unusual impairment movements, unexpected stage migration, abnormal default patterns, inconsistent recoveries, and portfolio-level anomalies.
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Evaluating automated ECL environments, data interfaces, model implementation, system calculations, model changes, access controls, and technology dependencies.
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Assessing AI-supported credit models and emerging technologies while considering explainability, data bias, model drift, cybersecurity, governance, and human oversight.
Module 9: ECL Audit Evidence, Controls, and Financial Reporting
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Evaluating internal controls over ECL model development, data extraction, staging, calculations, review, approval, validation, implementation, and financial reporting.
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Testing the completeness and accuracy of ECL model inputs and reconciling model populations and outputs to underlying loan systems, risk records, and accounting balances.
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Assessing impairment accounting entries, movements in allowance balances, write-offs, recoveries, and reconciliations to financial statements and supporting schedules.
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Reviewing financial reporting disclosures relating to credit risk, impairment, estimation uncertainty, assumptions, sensitivity, and movements in expected credit loss allowances.
Module 10: Integrated ECL Audit Case Studies and Practical Applications
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Performing an end-to-end ECL audit case involving portfolio segmentation, PD, LGD, EAD, staging, discounting, scenarios, overlays, and final impairment calculations.
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Testing a practical credit portfolio dataset to identify data-quality issues, unusual risk migrations, inconsistent classifications, unexpected losses, and model exceptions.
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Conducting independent ECL recalculations and sensitivity analysis to evaluate the reasonableness of management’s impairment estimates and significant assumptions.
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Preparing audit working papers, findings, recommendations, and conclusions covering model risk, data integrity, controls, financial reporting, estimation uncertainty, and remediation.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
