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Will Your Machine Learning Models Pass The William Tell Test?

August 14, 2018 at 8:34 AM

william tell

Machine learning models can be used very successfully in many different contexts to predict outcomes for different use cases accurately. These predictions can be used within the business to make better decisions or to operate more efficiently (or both) and can give you an edge over your competitors. Predictive models all follow the same recipe – i.e. train a model on historical data and then apply this model to unseen data to get predictions. If your model generalises well, you have a prediction that you can trust and use to decide "do this, not that" with some degree of accuracy.

Machine learning in financial services

In the financial services sector the most common requirement is to predict binary classifier outcomes – i.e. predicting a yes/no, True/False or a 1/0 outcome. Some examples include answers to these typical questions:

  • Shall we grant this applicant a loan?
  • Will this customer pay back their facility?
  • Will this customer attrite and move to a competitor?
  • Will the right customer answer this call?
  • Will this customer take up this new product?

There are many techniques out there that can provide varying levels of accurate predictions – e.g. logistic regression, support vector machines and neural nets. Principa has tried various techniques over time and we are seeing good results with the gradient boosted algorithm approach. This is a machine learning algorithm that is often the winning algorithm on the open competition website, Kaggle.com. There are numerous internal parameters that can be configured to fine-tune your model, plus it is fast (especially the Python libraries 'XGBoost’ and ‘LightGBM’) and their lightning fast speeds during the training phase allows one to run more experiments in the time you have available, giving you a better chance of finding the optimal tuning parameters. Check this out for a great illustration on how gradient boosted algorithms work:

 

XGBoost

XGBoost is happiest when the positive (e.g. responders) and negative (e.g. non-responders) classes are well balanced – i.e. you have around a 50% response rate. However, in our experience, this very seldom occurs. Take modelling fraud for example – there are generally very few positive classes (or fraudsters) on which to model, and this is often referred to as rare event modelling. This is quite an extreme case, but we still struggle with imbalanced classes, like response modelling or predicting a right-party-connect where the RPC rate is only around 1%. One can force balance in the algorithm by tuning the scale_pos_weight parameter. This will give you a good model that will separate the positive and negative classes quite nicely, but the problem with this approach is that that the resulting probability is going to be scaled incorrectly. So the RPC scores that fall in the 1-2% range are not going to average out at 1.5%, it will be something quite different. This is fine if you only want the model to help select the top records – i.e. you want the best 1,000 records out of a possible 10,000.

However, if your business strategy relies on the pin-point accuracy of your model's predictions, then this approach is not going to work for you. Fortunately, xgboost has many parameters to choose from that can be used to fine-tune the construct of the underlying algorithm. One of these parameters, the max_delta_step parameter can be used to great effect to give accurate point predictions in the case when the target variable is imbalanced.  We can show the impact of this in the views below using a right party connect (RPC) use case as the target that we want to predict. The first view shows a good model by tuning the scale_pos_weight parameter - the Gini coefficient for this model is a healthy 68.8%. But notice how poorly its prediction accuracy is (the blue line does not follow the perfect or unicorn model's green line in the second graph). When we tune the max_delta_step parameter, the model still separates the two classes nicely (with a Gini coefficient of 68.5% that is very close to the original model) AND gives good overall point prediction. We have seen real-world success following this approach on a few use cases now. If you would like skillful and reliable models that give you accurate predictions, contact us.

Scape Pos Weight 30-1

Max Delta Step 1

Using machine learning in business - download guide

Robin Davies
Robin Davies
Robin Davies was the Head of Product Development at Principa for many years during which Robin’s team packaged complex concepts into easy-to-use products that help our clients to lift their business in often unexpected ways. Robin is currently the Head of Machine Learning at a prestigious firm in the UK.

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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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