Expected Credit Loss (ECL) Modelling

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The move to an Expected Credit Loss (ECL) model is one of the most significant changes in financial reporting and credit risk management for banks and corporate lenders. It’s a transformative exercise that requires cross-functional support, with expertise in risk management, finance, IT, and economic forecasting particularly important.

At Grant Thornton Bharat, our ECL Advisory team works closely with organisations across the financial services sector to design and implement robust, scalable ECL solutions tailored to industry requirements and data maturity levels.

How to achieve high-quality ECL compliance

High-quality ECL compliance goes beyond building models that meet a checklist. Regulators, auditors, and boards increasingly expect ECL frameworks to be conceptually sound, operationally resilient and applied consistently across portfolios and reporting periods.

Getting this right usually requires five core areas: policy, data, models, governance, and execution.

01 Start with a clear and defensible ECL framework

A strong ECL implementation begins with a well-articulated framework that defines how credit risk is measured and when losses are recognised. This includes:

Clear staging and Significant Increase in Credit Risk (SICR) criteria.
Well-defined default definitions aligned with internal risk management practices.
Well-analysed segmentation of portfolios with similar risk characteristics.

A coherent framework ensures consistency, auditability, and alignment with business realities.

02 Build models that are fit-for-purpose

ECL models should reflect the nature of the portfolio and the availability of data.

In practice, high-quality models:

Are proportionate to portfolio risk and size.
Use transparent and explainable methodologies.
Balance statistical rigour with operational practicality.

Model sophistication typically evolves over time, supported by periodic reviews and enhancements.

03 Embed forward-looking and scenario-based thinking

Forward-looking information is central to ECL compliance and plays an important role in broader credit risk stress testing and portfolio risk assessment. Institutions must demonstrate that expected losses reflect multiple economic outcomes, not just historical trends.

This requires:

Selection of relevant macroeconomic variables.
Testing candidate models and approaches for incorporating forward-looking information.
Development of reasonable and supportable scenarios.
Transparent probability-weighting methodologies.

The focus is on plausibility, consistency, and governance, rather than excessive scenario complexity.

04 Strengthen data, systems, and processes

Data quality and process design often determine whether an ECL framework is sustainable.

Key considerations include:

Reliable historical data for defaults and recoveries.
Controls over data extraction, transformation, and aggregation.
Automated and well-documented ECL calculation workflows.
Well-designed processes reduce operational risk and ensure repeatability across reporting cycles.

05 Implement robust governance and controls

Governance is a critical differentiator between basic compliance and high-quality implementation.

Effective ECL governance includes:

Clear ownership across risk, finance, analytics and business teams.
Independent model validation and performance monitoring.
Regular reviews of assumptions, overlays, and expert judgements.
Strong documentation to support audit and regulatory scrutiny.
Governance structures should be proportionate but defensible.

06 Ensure transparency and explainability

High-quality ECL compliance requires institutions to clearly explain:

Drivers of movements in provisions.
Impact of macroeconomic assumptions.
Use of management overlays and judgment.

This transparency is essential for auditors, regulators, senior management, and boards.

How Grant Thornton Bharat can help

We help institutions achieve sustainable, high-quality ECL compliance by combining technical expertise, practical implementation experience, and regulatory insight. Our focus is on building ECL frameworks that are credible, auditable, and scalable.

Regulatory compliance with confidence

Our approach focuses on ensuring alignment with applicable accounting and regulatory frameworks, including IFRS 9, CECL and Ind AS 109, while maintaining clear model governance and robust documentation. We place strong emphasis on traceability from underlying data inputs through to final ECL outputs, supported by defensible assumptions and well-governed overlays. By working closely across accounting, risk, finance and technology teams, we help ensure that the ECL framework can withstand scrutiny from auditors, regulators and boards.

Automated and intelligent modelling

Manual data flows and fragmented processes often create operational risk and make ECL frameworks difficult to scale. Our approach replaces this with structured, tested modelling that combines statistical techniques with automated staging assessments, including SICR and default triggers, and scenario-based, probability-weighted calculations. Where advanced methods such as machine learning are used, they are applied selectively and with a clear focus on explainability. In practice, this leads to lower manual effort, more consistent and accurate results, faster close cycles and stronger validation outcomes, while ensuring that model outputs remain transparent and defensible.

Customisable risk models built around your portfolio

No two portfolios behave in the same way, and ECL models need to reflect those differences. We design and calibrate PD, LGD and EAD frameworks around the specific characteristics of each portfolio, whether retail, SME, wholesale or project finance, and taking into account product features such as secured versus unsecured exposures and revolving versus term structures. Our approach is aligned to an institution’s internal rating philosophy and risk appetite, and combines historical performance data with forward-looking macroeconomic inputs and management judgement overlays, so that model outputs reflect business reality rather than generic assumptions.

Seamless system integration

ECL implementation should fit naturally within an institution’s existing technology environment rather than disrupt it. Our approach focuses on integrating ECL models with core banking systems, existing credit risk engines, and finance and reporting platforms, supported by API-based data flows where appropriate. This allows institutions to build an architecture that can scale and adapt as regulatory requirements evolve. Whether operating on legacy infrastructure or more modern cloud-based platforms, the emphasis remains on minimising operational disruption during implementation.

Model development timelines

What to expect during ECL model implementation

Phase	Duration*	Key activities
1. Data assessment	Week 1-4	Evaluate data quality and integration feasibility
2. Model development	Week 5-14	Develop and train PD, LGD, and EAD models
3. Validation and testing	Week 15-20	Conduct scenario analysis and validate models
4. Deployment and KT	Week 21-24	Implement final model and documentation approval

^{*The projected timelines are for a mid-size bank. Timelines vary significantly based on the scale and complexity of the portfolio and specific system- and data-related considerations.}

Why choose Grant Thornton Bharat

Over the last 15 years, our people have worked across regions and banks of various sizes and complexities to develop and validate ECL models. Grant Thornton Bharat has invested in a dedicated team for IFRS 9/Ind AS 109 credit loss modelling, which brings together expertise in predictive modelling, technical accounting, and technology to help banks in their compliance with IFRS 9 principles. Grant Thornton is unique in the level of global integration of its ECL experts, which helps provide best-in-class ECL advisory support to its financial services clients. Our ECL modelling team has assisted large financial services institutions across India, the US, Europe, Asia Pacific, Middle East, Latin America and sub-Saharan Africa regions.

Our insights

Our Marquee event

ECL Masterclass

Grant Thornton Bharat’s flagship three-day masterclass on Expected Credit Loss (ECL) modelling brings together global experts and industry perspectives to deliver a deeply immersive learning experience. Designed for finance and risk professionals, the programme combines regulatory insights, advanced modelling methodologies, and practical case studies to help organisations strengthen their ECL frameworks.

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