IMPLEMENTATION OF EXPECTED CREDIT LOSS MODEL FOR NON-BANKING FINANCIAL COMPANIES

Introduction 

India has already embarked on the journey
towards adoption of Indian Accounting Standards (IndAS) with effect from the
financial year ended 31^st March, 2017 in two phases for prescribed
classes of companies other than the financial service entities. This journey
continues with the next phase of adoption of Ind As by Non-Banking Finance
Companies (NBFC) in two phases commencing from the accounting period
beginning 1^st April, 2018. Whilst there are several implementation
and transition challenges, by far the biggest challenge  for NBFCs lies in implementing and designing
an Expected Credit Loss model for making impairment provisions for financial
assets.

The initial plan of the MCA was to implement
Ind AS for the entire gamut of financial service entities covering NBFCs, banks
and insurance entities, which has been deferred by a year for banks and by two
years for insurance companies. Accordingly, the discussion in this article is
restricted only to NBFCs.

It may be pertinent to note that the RBI
had constituted a Working Group to deal with the various issues relating
to Ind AS Implementation by Banks which had submitted a detailed
report in September 2015, which may be equally important and
relevant to NBFCs since there is a fair degree of similarity in their business
models and the same would be also taken into account in the course of our
subsequent discussions. Apart from the said report there has been no
other regulatory guidance from the RBI or other sector specific regulators,
except from the National Housing Bank (NHB), which regulates Housing Finance
Companies, which is discussed subsequently.

DETERMINATION OF EXPECTED CREDIT LOSS (ECL)

NBFCs are currently mandated by the RBI to
follow a standardised rule based approach to determine the impairment of loans
in accordance with the prudential norms which requires classification of loans
into standard, sub-standard, doubtful and loss categories by prescribing the
minimum provisioning requirements under each category and hence the underlying
theme is the “incurred loss” model.

In contrast, Ind AS-109 dealing with
recognition and measurement of Financial Instruments has significantly modified
this approach for measuring and assessing impairment based on the entity’s assessment
of the expected credit loss over a 12 month period or for the entire duration
for all financial assets under amortised cost or FVTOCI category. However,
the RBI guidelines do mandate a minimum provision for different categories of
standard assets ranging from 0.4% to 2% which in a way is a form of an ECL
model, though the approach is quite different under Ind AS. This is expected to
be a game changer for all NBFCs and thus merits special attention in terms of
its approach as well as its implementation and transition challenges.

APPROACH TO DETERMINE THE ECL

As per Ind AS-109, ECL is required to be
computed on the basis of the probability weighted outcome as the present
value of the difference between the cash flows that are due to the entity in
accordance with the terms of the financial asset and the expected cash flows.
However, Ind AS -109 does not prescribe the methods or techniques for
computing the ECL and hence it is an area which is prone to a lot of subjectivity,
judgement and complexity for NBFCs.

It may be pertinent to note at this stage,
that in March 2012, the RBI had released a ‘Discussion Paper on Introduction
of Dynamic Loan Loss Provisioning Framework for Banks in India’  which provided a broad framework to compute
expected loss provisioning based on the industry average for some select asset
classes. Subsequently, vide its circular dated 7^th February,
2014 the RBI advised banks to develop necessary capabilitiesto compute their
long term average annual expected loss for different asset classes, for
switching over to the dynamic provisioning framework.

Whilst these guidelines (which are still
to be implemented) are applicable to banks, NBFCs may find it useful atleast in
the initial stages to refer to these guidelines for developing their own models
based on the broad computational principles as discussed below.

Mathematically, ECL can be represented as under:

ECL = EAD*PD*LGD

Where:

EAD refers to
the Exposure at Default or the Credit Loss

PD refers to Probability
of Default

LGD refers to
Loss Given Default

It would be pertinent at this stage to
discuss the principles, implementation issues and challenges for each of
the above concepts.

Credit Loss

Ind AS-109 defines credit loss as the difference between all contractual cash flows that are
due to an entity in accordance with the contract and all the cash flows that
the entity expects to receive (i.e. all cash shortfalls), discounted at the
original effective interest rate(or credit-adjusted effective interest rate for
purchased or originated credit-impaired financial assets).

An entity shall estimate cash flows by
considering all contractual terms of the financial instrument (for example,
prepayment, extension, call and similar options) through the expected life of
that financial instrument. The cash flows that are considered shall also
include cash flows from the sale of collateral held or other credit
enhancements that are integral to the contractual terms. There is a
presumption that the expected life of a financial instrument can be estimated
reliably. However, in those rare cases when it is not possible to reliably
estimate the expected life of a financial instrument, the entity shall use the
remaining contractual term of the financial instrument.

Assessing the credit loss / EAD is likely to
present several implementation and transition challenges in the Indian context,
the main ones of which are indicated below:

a)   Inadequate and inappropriate data: –
The existing guidelines for making provisions for NPAs are based on default
triggers based on time / due dates and may not always capture the estimated
cash flows from the facilities and related collaterals over the life of the
facility. It is thus imperative for NBFCs to evaluate their existing systems
and make suitable modifications and determine the additional data points
keeping in mind the time and cost constraints vis a-vis the benefits.

b)   Individual versus collective assessment:-
Whilst for the small ticket and individual facilities it may not be possible,
feasible and cost effective to assess the EAD for each facility, entities would
still need to group these facilities based on shared / common characteristics
like type of facility, type of borrowers, regional and other similar
considerations. However for corporate and large ticket loans, the assessment
would need to be done individually for which appropriate triggers for
classification would need to be laid down based on assessment of various
factors some of which may involve subjectivity and judgements. An indicative
criteria can be the assessment criteria laid down by the RBI under the BASLE II
and III guidelines for retail and non- retail classification by Banks for
assessing capital adequacy, which though not strictly applicable could be a
useful guide and accordingly for all non-retail exposures, the individual
assessment needs to be done.

c)   Cash flows from and realisability of
Collaterals:- This is likely to be by far the biggest challenge since
traditionally in our country enforcing of collaterals is a cumbersome legal
process which may in certain cases stretch upto a generation and beyond! These
and other similar factors would need to be assessed whilst evaluating the
present value of the cash flows. Also in many cases, fair value specialists
would need to be employed which would increase the costs and result in
significant judgements and potential bias which may vitiate the true picture.

Probability of Default (PD)

PD is an important constituent for computing
the ECL. However, the term is not specifically defined in the Ind As. It is a
financial term describing the likelihood of a default over a particular time
horizon. PD is the risk that the borrower will be unable or unwilling to repay
its debt in full or on time. The risk of default is derived by analysing the
borrower’s capacity to repay the debt in accordance with contractual terms. PD
is generally associated with financial characteristics such as inadequate cash
flow to service debt, declining revenues or operating margins, high leverage,
declining or marginal liquidity, and the inability to successfully implement a
business plan. In addition to these quantifiable factors, various qualitative
factors like the borrower’s willingness to repay along with factors like the
economic, business and industry factors relating to his area of operations also
must be evaluated.To summarise the PD is dependent on the overall credit
rating of the borrower which would factor in the above aspects, amongst
others.

Since it involves a fair degree of judgement
and estimation, entities would need to use appropriate internal statistical
models to assess the PD for various types of exposures over a 12 month
period or over the life of the exposure, as per the requirements laid down
under Ind AS discussed subsequently. This has also been reiterated by the RBI
in its Working Group Report. However, the report also refers to the Concept
Paper on Dynamic Provisioning, discussed earlier, which could be used as a
basis. The Paper has calculated the PD based on a study of data from 9 Banks
which represent approximately 40% of the total business under the following 4
categories of loans and worked out the PD. The only drawback in this
method is that it is calculated on the basis of the “percentage of
incremental NPAs during the year to the outstanding loans at the beginning of
the year”, whereas ideally the PD should be calculated based on the number of defaults
rather than the amount of default.

Weighted Average PD of Various Asset Classes

The above analysis though not entirely conclusive, fairly reflects the
differences in the PD based on the credit risks of the different types of
portfolio in the Indian scenario. However the same was based on a study which
is over five years old and the validity thereof, in the context of the current
economic and political environment, especially in case of corporate loans which
have a lower PD than retail remains questionable. Hence it is important for an
entity to have a dynamic and flexible statistical tracking mechanism.

Though the above discussion is in the
context of Banks, it may serve as a useful indicator / benchmark pending the
creation / generation of their own statistical models for NBFCs. However, NBFCs
are cautioned not to blindly use these without substantiating the same based on
data including, if required,  taking the
help of experts.

Loss Given Default (LGD)

Like PD, LGD is
also an important constituent for computing the ECL. However, the term like in
case of PD is not specifically defined in the Ind As. LGD is the amount of
money a lender loses when a borrower defaults on a loan. The most frequently
used method to calculate this loss compares actual total losses to the total
amount of potential exposure sustained at the time that a loan goes into default.
In most cases, LGD is determined after a review of anentity’s entire portfolio,
using cumulative losses and exposure for the calculation. For secured
exposures, it involves assessing the realisable value and assessing the
foreclosure amount of the collaterals, which as we have seen earlier can be a
challenge in our environment. In simple terms, LGD represents the economic
or business loss rather than the accounting loss.

Since it involves a fair degree of judgement
and estimation, entities would need to analyse the defaults and the losses
at an overall portfolio level which as discussed earlier can represent a
significant challenge for many Indian entities. This has also been
reiterated by the RBI in its Working Group Report. However, the report also
refers to the Concept Paper on Dynamic Provisioning, discussed earlier,
together with the Internal Ratings Based Approach under BASLE II for
determining Capital Charge for Credit Risks vide its circular dated December,
2011 by the RBI, to be framed by Banks, which could be used as a basis. The
Concept Paper has calculated the LGD based on a study of data of a
pool of NPAs from 9 Banks which represent approximately 40% of the total
business under the following 4 categories of loans and worked out the same. Whilst
the method is not entirely fool proof and free from doubt, it is a good initial
indicator prior to design of appropriate statistical models by the NBFCs.

Average LGD Estimates of Various Asset
Classes

Like in the case of the PD, the above
analysis though not entirely conclusive, fairly reflects the differences in the
LGD based on the credit risks of the different types of portfolio in the Indian
scenario. However, the same was based on a study which is over five years old
and the validity thereof, in the context of the current economic and political
environment, remains questionable. Further, the lower LGD in the case of
Housing Loans appears to be primarily due to the collateral value of the
property financed, the recovery and enforcement thereof may present challenges.
Hence it is important for an entity to have a dynamic and flexible statistical
tracking mechanism

As is the case with the calculation of
PDs, though the above discussion is in the context of Banks, it may serve as a
useful indicator / benchmark pending the creation / generation of their own
statistical models for NBFCs. However, NBFCs are cautioned not to blindly use
these without substantiating the same based on data including, if
required,  taking the help of experts.

STEPS TO CALCULATE THE ECL

The first step to calculate the ECL is to
classify the financial assets into different stages or buckets as tabulated
hereunder based on which the subsequent calculations for the extent of
impairment on ECL basis can be determined.

As can be seen from the above, the following
are the key triggers for assessing impairment on the basis of life time
expected credit losses:

Let us now proceed to briefly understand the
principles laid down in Ind AS-109 for assessing both these.

Increase in the Credit Risk

Whilst the assessment of increase in the
credit risk is qualitative and judgemental, IndAS-109 has laid down certain
principles which are summarised hereunder:

a) an actual
or expected significant change in the party’s external credit rating.

b) an actual
or expected significant change in the operating results of the party.

c)
significant changes in the value of the collateral supporting the obligation or
in the quality of third-party guarantees or credit enhancements, which are
expected to reduce the debtor’s economic incentive to make scheduled
contractual payments or to otherwise have an effect on the probability of a
default occurring.

Assessing Credit Impaired Financial
Asset:

For identifying receivables which are credit
impaired, Ind AS-109 defines a “credit impaired financial asset” as under:

“A financial asset is credit-impaired
when one or more events that have a detrimental impact on the estimated future
cash flows ofthat financial asset have occurred. Evidence that a financial
asset is credit-impaired include observable data about the following events:

(a) significant
financial difficulty of the issuer or the borrower;

(b) a breach of
contract, such as a default or past due event;

(c) the lender(s) of the
borrower, for economic or contractual reasons relating to the borrower’s
financial difficulty, having granted to the borrower a concession(s) that the
lender(s) would not otherwise consider;

(d) it is
becoming probable that the borrower will enter bankruptcy or other financial
reorganisation;

(e) the
disappearance of an active market for that financial asset because of financial
difficulties; or

f) the purchase
or origination of a financial asset at a deep discount that reflects the
incurred credit losses.

It may not be possible to identify a
single discrete event instead, the combined effect of several events may have
caused financial assets to become credit-impaired.”

One of the common criteria which is
practically applied in assessing credit impairment is to identify whether there
is a default or a past due event. In this context, Ind AS-109
provides that when defining default for the purposes of determining the risk of
a default occurring, an entity shall apply a default definition that is consistent
with the definition used for internal credit risk management purposesfor the
relevant financial instrument and consider qualitative indicators (for example,
financial covenants) when appropriate. However, there is a rebuttable
presumption that default does not occur later than when a financial asset is 90
days past due unless an entity has reasonable and supportable information to
demonstrate that a more lagging default criterion is more appropriate.

Accordingly, though the Ind AS
provides 30 and 90 day thresholds these are not sacrosanct like the existing
NPA guidelines and need to be evaluated in the context of other qualitative and
judgemental factors which need to be appropriately disclosed.  

Accordingly, it is imperative for NBFCs to
establish their own internal credit risk rating models, subject
to cost and volume considerations for different categories of risks, rather
than blindly adopt the 30 and 90 days rebuttable presumptions indicated above.
Let us now proceed to briefly examine the implementation and transition
challenges

IMPLEMENTATION AND TRANSITION CHALLENGES

Framing Internal Credit Risk Rating
Models

Currently in the case of NBFCs, there are no
specific regulatory guidelines which provide for the establishment of credit
risk management policies on the lines as prescribed by the RBI under the BASLE
II and III framework for Banks, except the generic requirement under the
Companies Act, 2013 and the Listing Guidelines to frame Risk Management
Policies. Accordingly, the transition from a rule based regulator specified
criteria approach that largely ensures consistency of application across the
system to an ECL framework that is largely subjective based on management
judgement and being data intensive, necessitates fairly sophisticated credit
modelling skills and would represent an enormous challenge not only for the
NBFCs but also for auditors, regulators and supervisors, especially for the
small and medium sized as well as closely held entities.

Accordingly, NBFCs are advised and expected
to develop their own internal credit risk rating models as part of their
overall Credit Risk Management Policies under the Supervision of the Board with
implementation support from the Risk Management Committee. For this purpose the
broad steps which need to be followed are outlined below:

a)   Framing an internal risk rating module for
different types of financial assistance and different types of financial
instruments, which evaluates each proposal for different types of risks,
security available, financial performance of the borrower etc.

b)   Based on the scrutiny of the proposals
against the above parameters a scoring module is developed which assigns scores
on a range of 1-10, 1-100 etc., which in turn is linked to a grade. An
illustrative scoring grid is as under:

Based on the above assessment any facility
granted to a borrower with a grading of C or D would represent increased credit
risk and hence would fall under stage 2 as discussed earlier thereby
necessitating a life time ECL calculation.

c)  A comparison of the above internally assessed
ratings can be compared with the externally assigned ratings to the borrowers
by the recognised external credit rating agencies.

d)  Periodic review of the above established
ratings through internal assessment coupled with audit assistance in certain
cases. For this purpose a review / assessment is undertaken of the servicing
and repayment of the facility, financial performance of the borrower, the
industry / business environment in which the borrower operates etc.

Normally, from a practical perspective, any rating down grade by more than
two notches would imply a default or credit impaired status necessitating the
movement of the exposure to stage 3 as discussed earlier, in addition to the
other specific qualitative parameters discussed.

The RBI working
group has discussed certain issues in the context of Banks pertaining to
identification of and the on-going assessment of increase in the credit risk as
under, which may be relevant and a useful indicator for NBFCs on initial transition,
subject to appropriate corroboration thereof with the existing data and the
peculiar nature of operations of each NBFC and pending any specific guidance
relating to NBFCs from the RBI:

a)  The Group suggested that the RBI could
prescribe rule based indicative criteria for significant deterioration in
credit risk.

b)  Whilst the group felt that the 30 days past
due scenario is quite common, Banks should take this opportunity to educate
their customers of making contractual payments within 30 days and also
simultaneously strengthen their credit monitoring mechanisms.

c)  In the context of the 90 days default
criteria, the Group suggested that RBI may continue to define default for
consistency across the banking system keeping in view the Basel framework as
well as the Ind AS 109 prescriptions. Banks may be permitted the discretion to
formulate more stringent standards.

d)  The Group also noted that Ind AS 109 envisages
other types of defaults, e.g., breach of covenants, which are not accompanied
by payment defaults. With respect to such defaults (not accompanied by payment
defaults), banks will need to build up adequate records to evidence the impact
of these events on the level of credit risk
and if these events constitute a significant increase in credit risk.

e)  Finally, the Group also
noted that RBI vide its circular DBOD.No.BP.520/21.04.103/2002-03 dated
October 12, 2002 had issued a Guidance Note on Credit Risk Management that
inter-alia advised banks to adopt credit risk models depending on their size,
complexity, risk bearing capacity and risk appetite, etc. and accordingly
advised Banks to adopt the same, since based on which it is reasonably expected
that banks should be able to put in place at least some basic measures of expected credit losses.

Regulatory Challenges

The NHB vide its circular dated 16^th
April, 2018 has broadly laid down the following requirements:

a)  In terms of the provisions of paragraph 24 of
the Housing Finance Companies (NHB) Directions, 2010 (“Directions”) on
Accounting Standards, in terms of which the Accounting Standards and Guidance
Notes issued by the Institute of Chartered Accountants of India shall be
followed in so far as they are not inconsistent with any of the Directions.

b)  All Housing Finance Companies to follow the
extant directions on Prudential Norms, including on asset classification,
provisioning etc. issued by the NHB.

c)  With regards to the implementation of Ind
AS, HFCs are advised to be guided by the extant provisions of Ind AS, including
the date of implementation.

A plain reading of
the aforesaid circular seems to suggest the following interpretation
alternatives:

a)  HFCs should continue to follow the existing
directions including the prudential and asset classification norms whilst
determining their capital adequacy ratio, which seems to imply that the working
as per the existing NPA norms would continue. Thus it appears that a
separate set of regulatory accounts would need to be maintained.

b)  For preparing the statutory accounts, the Ind
AS principles would need to be followed, which implies that the ECL model
should also be followed.

c)  Companies should accordingly adopt the
ECL model and compare the provision as per the same with the existing NPA
provisioning guidelines and the higher of the two should be followed since the
intention of the NHB seems to ensure that the regulatory minimum provisions
should be maintained. This is also the recommended alternative as suggested by
the RBI working group in its report.

There is currently no similar circular
which has been issued by the RBI for application by the NBFCs other than HFCs,
thereby creating a lot of ambiguity and leaving the field open to varying
interpretations, which could involve substantial time, efforts and costs which
may not be commensurate with the benefits and expose the NBFCs to potential
regulatory scrutiny.It is strongly recommended that appropriate clarifications
are issued by the RBI in this regard.

CONCLUSION

The above evaluation is just the tip of the
ice-berg on a subject that is quite vast and complex. However, the ECL model is
here to stay and it would impact the way the financial statements are evaluated
and also impact the auditors and prove to be a bonanza for specialists to
develop statistical models who could laugh all the way to the bank!