Due to the pandemic, it might become too burdensome or difficult for banks to determine the extent and adequacy of collaterals available with them and the subsequent provisioning. There may be additional disclosures required in the financial statements and the computation of capital adequacy for COVID-19.
Given the situation of the lock down in the world, the defaults may increase substantially as many companies would have lost revenue for a long time. An increase in defaults is likely to cause issues in liquidity and capital adequacy.
Banks would therefore be required to maintain robust risk management functions and track their borrowers individually to determine and segregate the permanent impact from the temporary impact and make appropriate provisions & revisit their hedging strategies.
The new impairment provision becomes applicable in times of high NPA levels and stressed asset situation experienced in the banking sector. The new impairment provision would require both financial services entities and the regulator to take a closer look at the impact on capital planning, pricing and alignment to risk management.
The ECL norms are likely to result in enhanced provisions given that they apply to off balance sheet items such as loan commitments and financial guarantees also.
The introduction of the forward-looking ECL model aligns the provision on financial assets consistent with their economic value and is more proactive during an economic downturn. However, the three stage credit loss recognition that requires advanced credit risk modelling skills and high quality data, poses a new challenge to many banks.
Financial assets are classified and measured on the following basis:
• Amortized Cost (AC);
•Fair Value Through Other Comprehensive Income (FVOCI)
•Fair Value Through Profit and Loss account (FVTPL)
Approaches for computation
The objective of impairment requirements is to recognize lifetime ECLs for all financial instruments for which there has been a significant increase in credit risk since origination. The assets which have not undergone any significant deterioration shall be recognized with only 12-month ECLs.
Portfolio segmentation approach will help banks in generating synergies both in the short term and the long term.
Banks can segment their portfolios into:
•Corporate loans (term loans, overdrafts, working capitalloans, LC renance loans)
•Retail loans (consumer, mortgage, vehicle and credit card)
•Agriculture loans (Kharif and Rabi crops)
•Investments (bank, sovereign and corporate)
•International banking division (loans to corporates overseas/other than domestic countries)
•Loans to banks and sovereigns
The retail portfolio shall be segmented by product types or pooled based on various individual and behavioral characteristics. The possible segments or the retail (mortgage, vehicle, credit card and consumer loan) portfolio of a bank can be listed as below:
•Public/private sector employee
•Income group of the borrower
•Collateral coverage ratio of the facility
ECL on financial assets is an unbiased probability weighted amount based out of possible outcomes after considering risk of credit loss even if probability is low. ECL can be defined as the difference between cash flows due under the contract and cash flows that an entity expects to receive. The ECL formula can be defined as following:
•Marginal Probability of default (MPD)
•Loss given default (LGD)
•Exposure at default (EAD)
•Discount factor (D)
Probability of default (PD)
PD is defined as the probability of whether borrowers will default on their obligations in the future. Historical PD derived from a bank’s internal credit rating data has to be calibrated with forward-looking macroeconomic factors to determine the PD term structure.
The forward-looking PD shall reflect the entities’ current view of the future and should be an unbiased estimate as it should not include any conservatism or optimism. The following list of methodologies can be used to generate forward-looking PD term structures:
•Markov chain model
•Parametric survival regression (Weibull model)
•Vasicek single factor model
•Forward intensity model on distance-to-default approach (public-listed firms)
•Pluto Tasche PD model (low/no default portfolio)
Loss given default (LGD)
LGD is an estimate of the loss from a transaction given that a default occurs different future periods.
LGD is one of the key components of the credit risk parameters based ECL model. In the context of lifetime ECL calculation, an LGD estimate has to be available for all periods that are part of the lifetime horizon (and not only for the case of a default within the next 12 months asunder Basel II).The LGD component of ECL is independent of deterioration of asset quality, and thus applied uniformly across various stages. The following methodologies are widely used to estimate LGD:
•Asset pricing model/Implied market LGD
Exposure at default (EAD)
EAD is one of the key components for ECL computation.
EAD can be seen as an estimation of the extent to which the financial entity may be exposed to a counterparty in the event of a default and at the time of the counterparty’s default.
EAD modelling would require the ALM system of the bank to produce either contractual or behavioural cash flows till the lifetime of the loans.
EAD shall also be modelled based on historical prepayments and establishing relationships with a change in interest rates to forecast the prepayment factors in order to estimate the expected payments in future scenarios.
For the funded/single drawdown exposures, the EAD modelling might not pose a challenge as compared to non-funded facilities.
EAD = Drawn line + credit conversion factor * undrawn
Difference between IAS 39 and IFRS 9
IFRS 9 and CECL Credit Risk Modelling and Validation: