There has been a lot of conversations about Banks and NBFCs seeking an extension for the roll-out of the Indian Accounting Standard (Ind-AS). Earlier scheduled to come into effect from April 1, 2018, it has been re-scheduled for April 1, 2019 for Banks. Financial Institutions are seeking extension citing higher capital requirements for bad loan provisioning, and delay in finalising of rules by the Reserve Bank of India as some of the reasons. Institutions are still exploring multiple approaches, and trying to understand the impact it has on their portfolio in the short and medium term.
In this blogpost, we try to break things down a bit.
It all started after the financial crisis of 2007-2008 when stakeholders of Banking & Finance industry across the world realized the need for a complete revamp of existing loan loss provisioning norms and practices. To address the systematic costs of delayed recognition of credit losses on account of “incurred loss” based approach, the International Accounting Standard Board (IASB) in 2014, published a new proactive standard on “Expected Credit Loss” for loan loss provisioning under IFRS9. The ECL standard is expected to come into effect between 2018 and 2021 across all major economies of the world, including India.
ECL, simply, is the present value of all cash shortfalls over the expected life of a financial instrument. Cash shortfall is nothing but the difference between the contractual cash flows which are due to an entity and the cash flows that the entity expects to receive. As per the standards, the measurement of ECL should reflect the following points:
In the context of India, Reserve Bank of India has directed all banks and Systematically Important NBFCs to adopt the ECL method for loan loss provisioning from April 1, 2018 in a phased manner. While for Banks, the timeline was extended by a year to April 1, 2019, NBFCs were expected to adopt the new method for reporting from April 1, 2018 itself.
Three parameters are important to arrive at ECL for any financial instrument – Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD). The product of PD, LGD and EAD results into ECL.
For a greater precision in ECLcalculations, it is required to be reported in three stages as thedeterioration in credit quality of the financial instrument takes place.
Stage 1 is called the “Performing Asset” and begins as soon as the financial instrument is initiated. Lenders are expected to calculate 12-month ECL as the portion of lifetime ECL that may result from the default events on the financial instrument within 12 months after the reporting date.
When the credit deterioration starts and the credit risk on the instrument increases, it moves into Stage 2 which is called as “Under-performing Asset”.
Eventually, when the instrument becomes credit impaired then it moves into Stage 3 which is known as “Non-Performing Asset”. For both Stage 2 and Stage 3, life-time ECL is calculated i.e. amount of losses expected to arise from the instrument during its remaining contractual life.
The new loan loss provisioning measure seems to be a justified step towards containing the growth of non-performing assets. ECL methodology will pave the way to identify the movement of bad performance of assets in stages, estimate the expected loss and take timely corrective action by adjusting the capital reserves, aligning to the impending negative economic prospects. In the short term, this new methodology will have a somewhat negative impact on all capital ratios of the financial institutions, however, in the long run it will help in weeding out the growth of non-performing assets from the books. While it is too early to say whether ECL is the all-cure method for mitigating NPA, it can definitely be stated that this new practice has sufficient ingredients to address the prevailing challenges of monitoring and controlling NPA. The robustness and performance of the method will be tested only when the banks and financial institutions of all major economies of the world have adopted the ECL method and its subsequent effect on their respective NPA growth has been ascertained.
ECLLens, our Artificial Intelligence (AI)-enabled ECL modelling solution, “ECLLens”, is a fast and efficientsolution that helps NBFCs:
Figure 1: Stage-wise PD & LGD
Figure 2: Visualization of Roll Forward from one bucket to next bucket
Figure 3: Quarter-wise ECL trends
To understand more about ECLLens and our smart lending solutions, contact email@example.com or call at +91 22 40935711