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IFRS9 Expected Credit Loss (ECL) Models For Retail Lending

February 12, 2019 at 8:50 AM

It is expected that IFRS9 adoption should lead to an increase in provisions (initially a balance sheet / retained earnings adjustment only with commentary on retrospective impacts). Typically, the increase is mostly a result of loss provisions for all accounts (regardless of a loss event) and the extension of the loss period from a typical 12 months to lifetime (e.g. for structured loans the remaining term of the loan plus time to default/write-off plus the recoveries window).

The below schematic provides an overview of the probable movements in provisions moving from IAS39 standard practices to an IFRS9 compliant solution:

Probable movements in IFRS9 Provision Calculations-1We’ve written extensively on IFRS 9, and in this blog, we’ll cover our IFRS 9 solution and process in depth.

Principa’s IFRS 9 Solution Project Phases

Principa follows four key project phases in providing an IFRS9 solution, as can be seen in the schematic overview of the process below.

IFRS9 Solution Project Phases

Phase 1: Business Overview: A review of the current environment

During phase 1 we conclude an initial review of the business in the context of provisioning. This includes getting an overview of the company, all business units and each lending product option. We'd also explore the data environment, check data availability and accessibility. The review would further extend to the business processes, credit risk management policies and practices, the current risk assessment toolkit (for both new and existing customers), the current IAS39 solution, -provision methodology and supporting principle and models, as well as the current provision coverage and any upfront IFRS 9 expectations and known compliance requirements or principle.

This phase would also include a review of any existing auditor observations, an offsite documentation review and an onsite workshop.

Phase 2:  IFRS9 Framework Design - IFRS9 gap analysis and roadmap to compliance

During phase 2, we’d aim to work with the client and their auditors to determine what the compliance principles are and what the final solution will look like. This phase ensures a smooth phase 3 and 4, and a clean audit after implementation.

This phase includes an IFRS9 requirements workshop to facilitate discussion around requirements principles and methodology; data extracts; defining, documenting and facilitating a management agreement of the IFRS9 compliance principles and methodology and the related roadmap; and defining the delivery approach, resourcing and timing and all project dependencies such as data requirements.

This would include the analytics required to provide supporting evidence to conclude the compliance principles, for example:

  • Determination of a significant finance component
  • Proposed IFRS9 approach: Simplified vs Generalised
  • Defining appropriate segmentation for the loans book
  • Defining the stage definitions
  • Confirming default / write-off / final loss definitions
  • Defining the lifetime period
  • Defining the recoveries performance window
  • Confirming the appropriate effective interest definitions and rates
  • Confirming discount rates
  • Unused exposure / commitment
  • Stage 3 Effective Interest recognition approach
  • Confirming most appropriated loss provisioning modelling methodologies (e.g. vintage based, portfolio run-off triangle, Markov, account level scoring)
  • Forward-looking approach

Phase 3:  ECL Model Development - Development and delivery of the IFRS9 compliant provisions solution

In phase 3, we move on to align data, definitions and assign stages and IFRS9 approach (simplified/general). As far as feasible, Principa will look to utilise the off-the-shelf Principa IFRS9 product solutions, for both the ease and speed to deployment, which can include:

  • Markov Loss Forecasting: A Markov based solution used to model expected future cash flows, per risk state, over the product’s credit life cycle.
    • This can be used to determine expected gross provisions.
    • Includes embedded revenue recognition outputs.
  • Recoveries Modelling: Run-off triangle solutions to determine expected recoveries over time
  • Forward-looking economic indicators modelling and scoring tool
  • Report and Disclosures reporting templates: Automated reporting relating to IFRS9 credit loss movements over time.
  • Standardised IFRS9 policy, model and user guide documentation templates.

Our bespoke solutions are tailored to meet our clients' needs, and where a client’s environment does not enable the use of these off-the-shelf solutions, alternatives will be proposed during Phase 2.

The Markov ProVision Solution

We develop a portfolio level propensity to default balance model to output default balances by month and specifically for at 12 months and lifetime.  Our Markov ProVision solution determines the cash flow movements between a range of risk states (defined analytically). This solution allows the user to determine expected future cash flows, per risk state, which is useful for budget setting, credit risk monitoring and management. Below is an illustration of the underlying logic of a Markov Chain Solution.

Markov Chain Solution

Recoveries modelling

We develop a portfolio level run-off triangle loss given default model (modelling expected post-default recoveries), which includes:

  • LGD1 for the loss given default at the time of default
  • LGD2 for the loss given default of the default portfolio of accounts

The following illustration shows how the data input for the run-off triangle solution is structured. The blue shaded areas indicate the recoveries relating to October 2017. Historical performance is used to determine an expectation of future recoveries, with the use of development factors (e.g. the relationship between the cash flows in area B are compared to those in area A to determine the likely cash flows to expect from month 4 to 5). The solution includes backtesting and could be used to monitor collections performance over time.

Data input for run-off triangle solution

Forward-looking economic indicators modelling scoring tool

Principa creates the capability to output a forward-looking scenario weighted final provisions. 

The forward-looking approach looks at modelling the relationship between macroeconomics and expected losses, both expected defaults and recoveries. This is done by using linear regressions to predict the 12-month error rates historically produced by the gross expected credit loss recoveries models. The underlying assumption is that these models (along with known business strategy change overrides) are not entirely accurate due to external factors that change the portfolio’s performance. As such, by resolving these errors with economic variables, we should be able to produce more accurate future provisioning results. This approach also ensures that there is no double counting of economic effects in the early performance of a portfolio already embedded in the forecast. Although the life of an account might be longer, the first 12 months typically represent a majority of expected credit losses. The underlying assumption is that the error rate of the model of the initial majority short-term losses represents the longer term error position.

Read more on the Key Considerations when Managing Your IFRS 9 Forward-Looking Overrides.

Report and Disclosures reporting templates

We define final provisions and overall provisions structure and conclude in the IFRS9 reporting suite.  The reporting coverage is:

  • Provisions Disclosure Summary Report:
  • Provisions Disclosure by Credit Risk
  • Provisions and Expected Credit Loss Key Metrics Over Time

The below image shows a summarised view of the IFRS9 Provision solution. Additional insights, in the broader reporting pack, allow you to assess why your provisions have increased/decreased over a specified period.

IFRS9 Provision Solution Summarised View

Standardised IFRS9 policy, model and user guide documentation templates

We provide the following documents and supporting material coverage of the IFRS9 solution:

  • IFRS9 Policy Framework Document – describing the policies and principles within which the provision model would function.
  • The IFRS9 Model Documentation – describing the development data, modelling approach and conclusions, including the validation results.
  • The IFRS9 Model User Guide – describing the monthly run procedure.

For further detail on the supporting documents and information we provide, read our blog Auditing Provisions: The Auditing Journey Over Time.

Phase 4: Deployment - Handover and Implementation support

After implementation, we provide backtesting to evidence model accuracy, deliver and handover the run procedures that includes a data solution (SAS / SQL) and excel total provisions calculation procedure and set in place detailed model write-ups covering both modelling methodology and run procedures. We also develop provision disclosure reporting, complete impact assessments, provide advice on provision governance structure and guidance to the impairment committee / executive committee through the sign-off for the adoption of the new provisioning procedures (IFRS9 compliance policies and procedure) and through the technical requirements for implementation into software for automation.

Our projects are delivered with a high level of collaboration between clients and Principa to ensure a business focussed solution that will pass audit standards. It is also recommended to have key milestone checkpoints with the auditors such as to agree on the development principles.

Get IFRS 9 Compliant

Edwin Cross
Edwin Cross
Prior to joining Principa in December 2013, Edwin had 13 years’ consumer credit (credit cards, revolving credit and personal loans) experience in Barclays Bank and Standard Bank SA with 10 years’ specialised in credit risk management across South Africa and Europe in particular United Kingdom, Spain, Germany and Italy of which over 3 years with Chief Risk Officer accountabilities. Edwin is considered an expert across the full customer life cycle and has lead and implemented a number of highly successful credit, marketing, collections, scoring and provisioning initiatives. Since joining Principa, Edwin continued to successfully deliver risk-based and profit-based initiatives to key clients (specialised lenders, large retail banks and retailers). He holds a M.Comm degree from Stellenbosch University.

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2021: Pursuing the pockets of profit

During the COVID-19 crisis, the media has focused much on the weak economy and stressed South African consumers. Figures show an increase in unemployment and for those lucky to be employed, many suffered decreased earnings through salary cuts. All this points to a highly strained economic environment.

Are we entering a mortgage provision spiral?

The South African credit bureau TransUnion recently released data on the performance of various different products within the bureau in their ”Quarterly Overview of Consumer Credit Trends” for the third quarter of 2020. With the COVID-19 crisis, 2020 was characterised by a severe reduction in account originations and payment holidays in Q2 with a high increase in non-performing accounts in Q3 as payment holidays ceased and stressed consumers failed to pay their accounts. The table below illustrates how each product showed worse performance (in terms of accounts moving to 3 months or more delinquent) year-on-year in Q3. For more on how Principa can assist your business in credit scoring and IFRS9 Provision click here and here. The table typically follows payment hierarchical patterns with credit cards performing best, but also illustrates risk-appetite for each product with clothing, microloans and retail instalments all showing the worst performance. For the retailers the closure of stores in Q2 meant fewer new good accounts were washing through, so the bad performing books in Q3 are/were accentuated. What does stand out, however, is the performance of mortgages that suffered a 350-basis point slump year-on-year in Q3. This is off a low overall “bad rate” too. Will the mortgage books bounce back, or will we see ourselves enter a mortgage provision spiral as we did in 2008/09? “When the spiral begins the knock-on effects can be catastrophic with provisions taking a hard hit.” Provisions in mortgages are unlike other product classes in consumer credit. When the spiral begins the knock-on effects can be catastrophic with provisions taking a hard hit. Banks around the world valued their books very differently post 2009 compared to pre-2008. A certain South African retail bank’s mortgage book valuation dropped by over 90% due to the knock-on effect of a mortgage provision spiral. Now the property market has been subdued for some years (compared to the bullish period leading up to 2008) so we are not expecting a mortgage crisis, but it is possible that a spiral will affect mortgages significantly as we enter a bearish market. How does the spiral work? An increase in defaults loans will mean the banks will need to make a difficult choice on whether to show leniency on the defaulting customers or to take strong action with repossession being the ultimate act. An increase in defaults also typically means that the book is not aging as expected and that the Probability of Defaults (PDs) experienced are higher than expected. Increase in defaults typically leads to more repossessions. More repossessions will mean the bank is left with an increased amount of stock (properties) to sell. More stock will likely mean bigger haircuts (i.e. difference between the net selling price of the property and its value) as the market becomes a buyer’s market. More stock together with the fact that banks will tighten lending criteria, will push property prices down. Bigger haircuts will mean an increase in shortfalls (i.e. where the net-value received for a property is less than the outstanding balance of the mortgage). More shortfalls will mean fewer voluntary sales to avoid defaulting (in bullish markets, consumers in financial destress may be pushed to sell their property; they’d likely make a profit from the property thus incurring no shortfall). Lower house prices will also contribute to more shortfalls and this in-turn results in much higher loss-given-defaults (LGDs). Higher PDs and LGDs pushes up provisions dramatically. Fewer voluntary sales to avoid defaulting means more accounts will now default and the spiral continues. The difference between a bullish and bearish market is illustrated in the image below. Whether we enter a bearish market and endure a mortgage spiral will depend on defaults increasing (generally due to the stressed South African economy) and whether banks enforce an increased number of repossessions. Whether we enter a bearish market and endure a mortgage spiral will depend on defaults increasing (generally due to the stressed South African economy) and whether banks enforce an increased number of repossessions.   Performance bounce back for the retailers At Principa we work closely with many retailers and we are aware that for many of them, Q3 saw accounts accelerate to a 3+ arrears state, but thereafter the book improved somewhat (i.e. those who were already stressed – accelerated to default – an inevitable ultimate state for some. On the other hand, the survivors are those resilient to the economic woes and continued to perform well; new accounts are also open). We look forward to establishing whether the same is true for mortgages when the performance figures are released for Q4. For more on how Principa can assist your business in credit scoring and IFRS9 Provision click on the links here and here or email us at info@principa.co.za.

The 7 types of credit risk in SME lending

  It is common knowledge in the industry that the credit risk assessment of a consumer applying for credit is far less complex than that of a business that is applying for credit. Why is this the case? Simply put, consumers are usually very similar in their requirements and risks (homogenous) whilst businesses have far more varying risk elements (heterogenous). In this blog we will look at all the different risk elements within a business (here SME) credit application. These are: Risk of proprietors Risk of business Reason for loan Financial ratios Size of loan Risk industry Risk of region Before we delve into this list, it is worth noting that all of these factors need to be deployable as assessment tools within your originations system so it is key that you ensure your system can manage them. If you are on the look out for a loans origination system, then look no further than Principa’s AppSmart. If you are looking for a decision engine to manage your scorecards, policy rules and terms of business then take a look at our DecisionSmart business rules engine. AppSmart and DecisionSmart are part of Principa’s FinSmart Universe allowing for effective credit management across the customer life-cycle.  The different risk elements within a business credit application 1) Risk of proprietors For smaller organisations the risk of the business is inextricably linked to the financial well-being of the proprietors. How small is small? The rule of thumb is companies with up to two to three proprietors should have their proprietors assessed for risk too. This fits in with the SME segment. What data should be looked at? Generally in countries with mature credit bureaux, credit data is looked at including the score (there is normally a score cut-off) and then negative information such as the existence of judgements or defaults; these are typically used within policy rules. Those businesses with proprietors with excessive numbers of “negatives” may be disqualified from the loan application. Some credit bureaux offer a score of an individual based on the performance of all the businesses with which they are associated. This can also be useful in the credit risk assessment process. Another innovation being adopted internationally is the use of psychometrics in credit evaluation of the proprietors. To find out more about adopting credit scoring, read our blog on how to adopt credit scoring.   2) Risk of business The risk of the business should be managed through both scores and policy rules. Lenders will look at information such as the age of company, the experience of directors and the size of company etc. within a score. Alternatively, many lenders utilise the business score offered by credit bureaux. These scores are typically not as strong as consumer scores as the underlying data is limited and sometimes problematic. For example, large successful organisations may have judgements registered against their name which, unlike for consumers, is not necessarily a direct indication of the inability to service debt.   3) Reason for loan The reason for a loan is used more widely in business lending as opposed to unsecured consumer lending. Venture capital, working capital, invoice discounting and bridging finance are just some of many types of loan/facilities available and lenders need to equip themselves with the ability to manage each of these customer types whether it is within originations or collections. Prudent lenders venturing into the SME space for the first time often focus on one or two of these loan types and then expand later – as the operational implication for each type of loan is complex. 4) Financial ratios Financial ratios are core to commercial credit risk assessment. The main challenge here is to ensure that reliable financials are available from the customer. Small businesses may not be audited and thus the financials may be less trustworthy.   Financial ratios can be divided into four categories: Profitability Leverage Coverage Liquidity Profitability can be further divided into margin ratios and return ratios. Lenders are frequently interested in gross profit margins; this is normally explicit on the income statement. The EBIDTA margin and operating profit margins are also used as well as return ratios such as return on assets, return on equity and risk-adjusted-returns. Leverage ratios are useful to lenders as they reflect the portion of the business that is financed by debt. Lower leverage ratios indicate stability. Leverage ratios assessed often incorporate debt-to-asset, debt-to-equity and asset-to-equity. Coverage ratios indicate the coverage that income or assets provide for the servicing of debt or interest expenses. The higher the coverage ratio the better it is for the lender. Coverage ratios are worked out considering the loan/facility that is being applied for. Finally, liquidity ratios indicate the ability for a company to convert its assets into cash. There are a variety of ratios used here. The current ratio is simply the ratio of assets to liabilities. The quick ratio is the ability for the business to pay its current debts off with readily available assets. The higher the liquidity ratios the better. Ratios are used both within credit scorecards as well as within policy rules. You can read more about these ratios here. 5) Size of loan When assessing credit risk for a consumer, the risk of the consumer does not normally change with the change of loan amount or facility (subject to the consumer passing affordability criteria). With business loans, loan amounts can range quite dramatically, and the risk of the applicant is normally tied to the loan amount requested. The loan/facility amount will of course change the ratios (mentioned in the last section) which could affect a positive/negative outcome. The outcome of the loan application is usually directly linked to a loan amount and any marked change to this loan amount would change the risk profile of the application.   6) Risk of industry The risk of an industry in which the SME operates can have a strong deterministic relationship with the entity being able to service the debt. Some lenders use this and those who do not normally identify this as a missing element in their risk assessment process. The identification of industry is always important. If you are in manufacturing, but your clients are the mines, then you are perhaps better identified as operating in mining as opposed to manufacturing. Most lenders who assess industry, will periodically rule out certain industries and perhaps also incorporate industry within their scorecard. Others take a more scientific approach. In the graph below the performance of an industry is tracked for two years and then projected over the next 6 months; this is then compared to the country’s GDP. As the industry appears to track above the projected GDP, a positive outlook is given to this applicant and this may affect them favourably in the credit application.                   7) Risk of Region   The last area of assessment is risk of region. Of the seven, this one is used the least. Here businesses,  either on book or on the bureau, are assessed against their geo-code. Each geo-code is clustered, and the projected outlook is given as positive, static or negative. As with industry this can be used within the assessment process as a policy rule or within a scorecard.   Bringing the seven risk categories together in a risk assessment These seven risk assessment categories are all important in the risk assessment process. How you bring it all together is critical. If you would like to discuss your SME evaluation challenges or find out more about what we offer in credit management software (like AppSmart and DecisionSmart), get in touch with us here.