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How to COVID-proof your scorecards with short-outcome machine learning models

July 21, 2020 at 12:20 PM

“Unprecedented” is a term with which we’ve all become quite familiar over the last few months. COVID-19 has changed our society and our economy quite drastically. In predictive analytics “unprecedented” has far reaching implications – simply put it’s difficult to build models when we do not have data that reflects similar trends to what we will expect moving forward.

So, it’s very unlikely that the models that you have deployed – whether it be in originations, account management, IFRS9 or collections are working as expected. In 2 previous blogs here and here we covered how data is changing during the COVID-19 period.

What to do?

The first thing to do would be to conduct a model HealthCheck. Principa’s Analytical ICU will assess the health of your models and triage them into four categories. At the very least you will need to realign your models, but the likelihood is that you may need to fine-tune or rebuild your models completely.Rebuilding your models

If you decide you need to rebuild your models, then you have a challenge at hand.  The diagram below represents a scorecard build time-line. The observation and outcome period are the periods from which we would extract data for the scorecard build. What is evident is that the observation period and outcome period do not coincide with the COVID-19 crisis. This means that the scorecards that you would rebuild may not be suitable for the current economic climate and you are back at square one.There is a solution, though, and that is to adopt machine learning models and to approach the scorecard build with a “short-outcome/strict-performance” methodology.

Short-outcome/ strict-performance

A short-outcome/ strict-performance approach will involve sampling the data from a shorter period of time and use a very strict performance definition (e.g. Any missed payment = ”Bad”).  This approach will allow you to sample from a period that is more representative of the current environment. The time lines below illustrate that a scorecard built in August/September could utilise April/May observations (i.e. the beginning of the COVID-19 period).One of the reasons one uses a long observation period is to take in the full annual cycle. As we are looking at catering for the COVID-19 “cycle” the shorter term is more appropriate. The common good/bad definition (for example “ever 3+” = “Bad”) is better, as it allows for the separation of truly good payers from truly bad payers. The stricter definition means that you’ll pick up technical arrears and a few lazy payers in your bad definition. This may weaken the models slightly, but the gains from being able to model for the COVID-19 period should outweigh the losses from the lesser performance definition. Another challenge with the short-outcome models will be the population size. The modelling approach will be different with the smaller population and we may use coarser classing.

Model longevity and machine learning

Once you deploy these “COVID-19” models, the models should be monitored. Redevelopment will likely need to happen sooner than traditional models. It is therefore suggested that Principa’s Quick-Step machine learning models would be most appropriate here allowing you to leverage off new COVID-19 data every quarter and reducing the cost of a sorecard-build.In our next blog we will be covering Principa’s Quick-Step Machine Learning and why this is a great solution to switch models in-and-out during the COVID-19 recovery period.

To find out how Principa can help you with an Analytics ICU or the building of short-outcome/ strict-performance credit models, contact us on info@principa.co.za.

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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.

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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. 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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.

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Principa Decisions (Pty) L