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Automation And Machine Learning: How Much Is Too Much?

November 12, 2017 at 1:33 PM

At Principa we’ve become quite passionate about Artificial Intelligence and Machine Learning. Recently quite a bit has been published in the press about how automated machines should be allowed to get.  Most famously perhaps there have been the warnings from the likes of South African born Elon Musk and theoretical physicist Professor Stephen Hawking.

"AI is the rare case where I think we need to be proactive in regulation... by the time we are reactive... it'll be too late." - Elon Musk

While we don’t anticipate our machine learning engine morphing into Skynet any time soon(!), there are nevertheless very important questions that we are tackling at the moment.  While I won’t cover all of these issues in this post, I do want to talk about a couple of them. 

“Explainability” – a credit example

The first issue is around “explainability”.  For those who have built scorecards before you will be aware of this notion.  For example, if I was to build a scorecard for credit applications, a popular characteristic to include would be “age of applicant”.  Most scorecards would recognise a monotonic relationship – i.e.  typically, the older you are the better risk you’re likely to be. This is an easily explainable trend if you appreciate that older people are typically more financially secure than younger people. So if we build a scorecard and the resultant model represents this trend then we would happily accept this characteristic into the scorecard.


Conversely we may find very different trends (albeit predictive and possibly stable when checked against the hold-out sample).  If we are unable to explain the trends then either the characteristic will be rejected from the scorecard, or the attribute groups would need to be re-classed into “explainable” groups.

When we chat to clients about these approaches the vast majority (particularly those subject to Principa2.pngheavy degrees of compliance) agree that all trends should be explainable. There are others that refute this as an “argument from personal incredulity” and tend to trust the trends observed (subject to the trends validating against a hold-out sample).

For credit models, we tend to take the conservative (former) approach, but this approach is difficult to implement within a machine learning environment (how do you model “common sense”?). That is why we manually check our models for the unexplainable once the machine has retrained the model.  

"How do you model common sense?"


Another approach that we have employed is that we tend to deploy our models as ensemble models (i.e. incorporated with a previous tried-and-tested model). That might mean that we take 80% of the original Generation 1 score and 20% of the Machine Learning (Generation x) score to create an ensemble score (subject to both scores being scaled on the same score/odds scheme).  In that way we can ensure confidence that our new models will add some lift, but will not create unwanted instability. Other ensembling approaches are also employed.

For the 1st time in human history we are beginning to develop tools, the workings of which no-one understands.

It’s both frightening and exciting. While we are very excited about what machine learning is bringing to the market, we are cautious and employ a level of manual assessment and belts-and-braces in each of our ML assignments.  However, as our applications get wider and we delve into more complex data where it is very difficult to fully understand each trend, then what do we do?  Our philosophy is to develop or determine an approach project-by-project, but always take a conservative fall-back position.

Using machine learning in business - download guide

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. 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.

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