ECL 3.0 -Expected Credit Loss Solution

Discover the power of ECL 3.0, the advanced solution from ICRA Analytics designed to revolutionise the computation of the expected credit loss (ECL). With ECL 3.0, you can unlock efficient risk management by automating complex calculations and ensuring accurate measurement of key components like probability of default (PD), loss given default (LGD), and exposure at default (EAD).

Say goodbye to manual, time-consuming processes, as ECL 3.0 reduces the lead time from days to minutes, providing real-time ECL visibility at various levels. Trusted by top private and public sector NBFCs, ECL 3.0 aligns with regulatory compliance requirements, enabling you to stay ahead in today's dynamic financial landscape. Streamline your risk management processes with our ECL 3.0 solution.

We have already computed the ECL for over 1.5 million borrower accounts, encompassing diverse loan portfolios including vehicles, housing, personal, micro, small and medium enterprises (MSME), and infrastructure to large corporates, with our experience spanning a period of more than five years.

Read our article on ‘Why ECL 3.0 is the Ultimate Solution to Automate ECL Computation here

Features

IFRS 9/Ind AS 109 compliant with improved process governance and transparency

Processes data in Excel format while adhering to IT standards and policies

Intuitive reporting and analytics for management, auditors, and other stakeholders

Integrates with legacy systems like LOS, learning management system (LMS), and core banking systems

Dedicated support to professionals with expertise in ECL computation

Provides granular ECL visibility at the borrower, account, zone, and region levels

Unique Selling Propositions

Portfolio segmentation and staging policy
12-month and lifetime probability of default computations
Forward-looking adjustments based on multiple macroeconomic scenarios
Loss given default computation based on recovery of cash flows and collateral valuation approach
Exposure at default computation based on future cash flows
Probability weighted expected credit loss computation and reporting

Used by

Banks

NBFCs

SMEs

FAQs

An Expected Credit Loss solution calculates forward-looking credit loss provisions across loan portfolios. It typically combines exposure at default, probability of default, loss given default, staging rules, macroeconomic scenarios and management overlays to support risk, finance, audit and regulatory reporting.

ECL software supports IFRS 9 and Ind AS 109 compliance by automating staging assessment, lifetime and 12-month ECL calculation, PD/LGD/EAD application, scenario weighting, disclosures and audit trails. It helps create a consistent bridge between risk models, accounting requirements and management reporting.

ECL automation benefits banks and NBFCs by reducing manual spreadsheet dependency, improving calculation consistency, shortening reporting cycles and strengthening governance. It also enables better analysis of stage movement, portfolio-level expected loss, sensitivity to assumptions and period-on-period provision changes.

ECL analytics improves credit risk visibility by showing how expected losses are distributed across products, ratings, sectors, geographies, stages and borrower segments. This helps management identify concentration, deterioration, macroeconomic sensitivity and provision drivers more clearly.

ECL 3.0 can support multiple lending portfolios such as housing loans, vehicle loans, personal loans, MSME loans, loan against property, infrastructure finance and corporate exposures. The framework should allow portfolio-specific segmentation, assumptions, model parameters, staging criteria and reporting templates.

ECL reporting tools help financial institutions generate structured outputs for management, finance, auditors and regulators. They should provide provision summaries, stage-wise movement, parameter assumptions, overlays, reconciliations, exception reports and audit trails to support review and sign-off.

Forward-looking credit loss estimation incorporates expected future economic and borrower-specific conditions into loss estimates. Instead of relying only on historical default experience, it considers scenarios, macroeconomic variables, portfolio trends and management overlays to assess likely credit losses.

Credit loss analytics supports risk management by quantifying expected losses, identifying high-risk segments, analysing provision drivers and highlighting deterioration trends. It helps institutions strengthen portfolio monitoring, financial planning, stress analysis and management action.