The Data Analytics Blog

Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

All Posts

10 ways the COVID-19 crisis will affect your credit models (PART 1)

April 5, 2020 at 12:25 PM

Black Swan

One of the basic principles of credit scoring and modelling is that the “future is like the past”.  Whilst robust credit models may be calibrated on multiple time periods, this assumes that trends in the past represent what is going on today.  COVID-19 is a black swan event – meaning in the modern day it really is unprecedented.  If you have never come across the term black swan, or if you have but no idea the origin, I recommend taking two minutes to read its really interesting etymology.

Over the last 20 years we have witnessed hundreds of our clients move from manual and rudimentary strategies to sophisticated, model-driven and automated ones. This had occurred across the customer lifecycle from acquisitions (hunting for new customers), originations (on-boarding), to account managing, financial models including IFRS9 provisions, and finally collections and recoveries. 

At Principa we live and breathe models. In this blog we list 10 key ways how we believe COVID-19 will affect your credit models. It’s in two parts, the first part deals with scoring across the lifecycle. The 10 items are as follows:


  1. Bureau scores
  2. Acquisitions scores
  3. Application scores
  4. Behavioural scores
  5. Collection scores


  1. Payment holidays
  2. IFRS9
  3. Sales and budgeting models
  4. SMEs
  5. Vintages

In a future blog we will cover some remedial actions and dive deeper into some of these topics. 

We’ll be discussing the contents of the blog in a series of videos.  To receive them you can subscribe to our YouTube channel or you can sign up here. 

If you would like to chat to us to understand how we might be able to help you with your pressing concerns, please contact us here.

  • Credit Bureau scores drop

Credit scores are used across the credit lifecycle, but most notably in originations. As more individuals miss payments, so their credit score will drop.  Does this represent a heightened risk or is this temporary effect due to the COVID-19 crisis? Does the score calibration represent the actual risk I should expect 12 months from now?  It should be noted that the utilisation of payment holidays will not affect variables utilising amount paid or percentage paid but will affect variables that utilise a worsening of cycle delinquent status (the latter variables are frequently used in bureau scores).  

In summary we expect credit bureau scores to drop as the overall risk of the population does drop, but not necessarily in line with the expected score-to-odds relationship as per the pre-COVID19 calibration.  We also expect some higher risk customers to benefit from the payment holidays which may flatten out the score-to-odds relationship.

We discuss this further in a VLOG that you can view here.
  • Acquisition: Risk and Response models

Along with the COVID-19 crisis, South Africa has also experienced two credit agency downgrades. These have all bought much stress to the South African consumer and will affect the taking up of credit offers.

Acquisition models typically comprise a response model (to a campaign) along with a risk model.  For insurance early attrition models may also be used. Just as the bureau scores and application scores may underestimate risk, so the risk acquisition models may do the same initially.  Over time we’d expect the score distributions to deteriorate too.  For response models it very much depends on the product. There may be an increased interest in unsecured loans as consumers experience more economic stress: so, response models may under estimate response to unsecured credit campaigns. Insurance, apart from health insurance or income protection may drop in response. Ultimately these models will need to be recalibrate (or preferably rebuilt) to deal with the new-normal post COVID-19.

  • Application scorecards

Application scorecards will also be affected by the COVID-19 crisis.  Whilst demographic data may not change, the score-to-odds ratios may indeed change, and the scorecards will ultimately need a “down-turn” calibration.  Self-employed individuals may be at higher risk than normal. Scorecards will likely “flatten out” – i.e. may not rank-order risk in the same way. Therefore, aggressive risk-based-pricing strategies may not be as prudent as the underlying risk at each scoreband/risk-grade may not be the same as previously expected.  If you are not monitoring or only monitoring your scorecards annually/biannually then we’d recommend you make this a monthly activity during this crisis and address any aggressive risk-based-pricing strategy.

  • Behaviour scores dropping for the highly stressed population

Well-made behaviour scores should have been built utilising data through the credit cycle.  However, Corona-type shocks do not exist in recent historic data. It should therefore be expected that behavioural scores are likely to drop for a considerable number of customers over the next twelve months at least. Payment behavioural patterns will also be impacted with payment holidays and the scores might flatten out (with otherwise low scoring customers scoring a little higher).  Regular (monthly or even weekly monitoring) and more conservative strategies are recommended.

  • Collection models

Collections models are likely to be affected on a variety of fronts. Some of the key changes will be the following:

  1. Severe operational changes during the COVID-19 lockdown will likely see higher roll-rates than expected as fewer delinquent customers are contacted. The models will therefore over-estimate probability of payment or under predict probability of rolling.
  2. Stressed population will likely see a lower propensity-to-pay or higher probability of default than what the models originally predict.
  • Payment projection scores for customers in late stage may underestimate total recovered amount for accounts particularly with a sustained period of hardship and lack of contact during the initial phases
  1. Right-person-connect and right-time-to-call models will predict differently during the lock-down phase as customers are staying at home.
  2. Preferred instalment models may over-estimate affordable instalment as customers’ cashflows are severely affected during this period.
  3. Optimal settlement models may over-estimate affordable settlement amounts and this is likely to continue during the sustained period of hardship expected.

Some models may require redevelopment, some a recalibration and some (like connect models) may experience a short period of inaccuracy but will then rebound to normal. ALL MODELS SHOULD BE MONITORED MONTHLY.


Whilst black swan implies an event, we believe that the COVID-19 crisis will be long-lasting, and the effects felt long after the crisis has abated. We would strongly recommend as a lender you consider addressing these effects by assessing them and taking remedial action.  If you would like to chat to us to understand how we might be able to help you with your pressing concerns, please contact us here.


Contact Us to Discuss Your data analytics Business Requirements

Thomas Maydon
Thomas Maydon
Thomas Maydon is the Head of Credit Solutions at Principa. With over 17 years of experience in the Southern African, West African and Middle Eastern retail credit markets, Tom has primarily been involved in consulting, analytics, credit bureau and predictive modelling services. He has experience in all aspects of the credit life cycle (in multiple industries) including intelligent prospecting, originations, strategy simulation, affordability analysis, behavioural modelling, pricing analysis, collections processes, and provisions (including Basel II) and profitability calculations.

Latest Posts

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.

Collections Resilience post COVID-19 - part 2

Principa Decisions (Pty) L

Collections Resilience post COVID-19

Principa Decisions (Pty) L