The Expected Credit Loss (ECL) framework represents a forward-looking approach to recognising credit risk and impairment in financial assets. Under IFRS 9 and Ind AS 109, institutions are required to assess potential credit losses based not only on historical experience, but also on current conditions and reasonable forward-looking information. This shifts impairment assessment from a reactive model to one that anticipates risk over the life of an exposure.
A robust ECL framework requires disciplined modelling, sound assumptions, and strong governance to ensure accuracy, transparency, and consistency in financial reporting. Actuarial and analytical techniques support segmentation of portfolios, incorporation of macroeconomic scenarios, and assessment of model performance over time. Together, these elements enable organisations to meet accounting and regulatory expectations while providing management and stakeholders with a clear view of credit risk and its financial impact.
Tailored solutions for stakeholders across the financial reporting ecosystem.
Retail and wholesale credit exposure with forward-looking impairment assessment.
Trade finance, leasing, and structured credit products.
Premium receivables, reinsurance recoverables, and counterparty credit risk.
High-volume, data-driven credit portfolios requiring robust ECL models.
Support is provided in valuing loss allowances for financial assets subject to impairment, including loans, receivables, and other credit exposures under IFRS 9 and Ind AS 109. Actuarial and statistical techniques are applied to portfolios segmented by risk characteristics and exposure types. The approach incorporates forward-looking information and macroeconomic scenarios to estimate provisions in a manner that is transparent, consistent, and aligned with accounting requirements.
ECL models are designed, developed, or enhanced to reflect the specific characteristics of an institution’s portfolio. This includes defining model architecture across probability of default (PD), loss given default (LGD), and exposure at default (EAD), supported by historical data, behavioural patterns, and relevant external indicators. The objective is to improve predictive accuracy while ensuring alignment with accounting standards and regulatory expectations, including those issued by the RBI, and maintaining scalability as risk profiles evolve.
ECL models are designed, developed, or enhanced to reflect the specific characteristics of an institution’s portfolio. This includes defining model architecture across probability of default (PD), loss given default (LGD), and exposure at default (EAD), supported by historical data, behavioural patterns, and relevant external indicators. The objective is to improve predictive accuracy while ensuring alignment with accounting standards and regulatory expectations, including those issued by the RBI, and maintaining scalability as risk profiles evolve.
Independent model review support is provided across products and portfolios. This includes testing model assumptions, segmentation logic, calibration techniques, and scenario application. Models are benchmarked against industry practices and regulatory guidance, with findings documented in a structured model efficacy report that identifies gaps and recommends corrective actions.
Assistance is provided in establishing a structured governance framework for ECL models across their lifecycle. This includes defining roles and responsibilities for model owners, users, and validators; setting up approval, review, and change control processes; and monitoring model performance and recalibration needs. The framework supports accountability, audit-readiness, and ongoing regulatory compliance.
Assistance is provided in establishing a structured governance framework for ECL models across their lifecycle. This includes defining roles and responsibilities for model owners, users, and validators; setting up approval, review, and change control processes; and monitoring model performance and recalibration needs. The framework supports accountability, audit-readiness, and ongoing regulatory compliance.
Comprehensive documentation is developed to support transparency, traceability, and audit requirements. This includes technical documentation covering model design, assumptions, inputs, and outputs; audit trails for model development and updates; and user manuals and governance logs. The documentation supports internal review processes, external audits, and effective knowledge transfer.
Scorecard models are developed to assess borrower default likelihood using historical data and behavioural indicators. Statistical and machine learning techniques are applied to differentiate risk levels across borrowers and portfolios. The scorecards are designed to integrate with credit decisioning and portfolio monitoring systems, supporting improved risk segmentation and proactive credit risk management.
Scorecard models are developed to assess borrower default likelihood using historical data and behavioural indicators. Statistical and machine learning techniques are applied to differentiate risk levels across borrowers and portfolios. The scorecards are designed to integrate with credit decisioning and portfolio monitoring systems, supporting improved risk segmentation and proactive credit risk management.
The consultants behind our precision
Lead – Actuarial Business Analytics
ganesh@ka-pandit.com20+ years specializing in post-retirement benefit valuations for Fortune 20+ years specializing in post-retirement benefit valuations for Fortune.
Associate Actuary
Senior Lead – Business Development
20+ years specializing in post-retirement benefit valuations for Fortune 20+ years specializing in post-retirement benefit valuations for Fortune.
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Cashflow Projections (ALS Study)
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