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How To Take Pro-Active Measure Using Call Centre Yield Predictions

May 14, 2018 at 4:27 PM

For collection operations and risk alignment, a critical success factor is the ability to predict month-end results accurately and at an early stage of the billing cycle.

This has always been a much-contested topic, due to the nature of accurate predicting early in a billing cycle.

The norm of predicting results in operations has always been a combination of tracking performance points to last-month-same-time, last-3-months-same-time and last-year-same-time, but also to take seasonality and unexpected anomalies into consideration.

While using data is a good start, basic analysis is often unreliable and insufficient to accurately forecast performance. It creates inter-month inaccuracies resulting in an inaccurate month-end expectation. Standard comparisons won’t give you the answers you need to create a strategy that will see the desired results, and it often doesn’t identify areas to implement pro-active remedial action and when to implement these actions to achieve your desired result.

The next question would be What does? Is there an alternative?

And yes, there is an alternative: big data analytics with machine learning capability.

Machine learning driven forecasts

By using predictive analytics and machine learning methods, it is possible to forecast yields accurately, and predict which areas need remedial actions in the short or long term to have an impact if you are not tracking favourably to your month-end expectations. You can use these insights to inform your business and collections strategy very effectively.

Tools that offer this are straightforward to use and easy to understand. Based on your call centres past performance, machine learning can predict what your results will be in the current month and identify problematic areas.

By graphically plotting metrics and predictions against targets, it's also effortless to understand the data at a glance.

How are operational problem areas identified?

By accurately predicting month end results (in contrast to month-to-date results) early in the billing cycle, you can identify overall problems if collections will come in below target.

If performance predictions indicate overall unfavourable month-end results, a more granular inspection should be done, and individual portfolios/areas evaluated in the same way as overall performance. This will help identify which portfolios/area are under pressure, and require prioritised focus. This granular breakdown will also give you an overview of the most and least successful portfolios, and help you prioritise your workforce accordingly.

It is also essential to identify which performance metric (connects, Right-Party-Connects, Promise-to-Pay etc.) can be linked to the predicted unfavourable performance. Some performance metric intervention requires "back-office" focus as opposed to call centre agent performance focus (matters worked, negotiation etc.).

By breaking your targets down to a granular level, you’ll be able to identify both portfolio and performance metrics that are in need of pro-active remedial action. (Click to Tweet!)

How will I know whether the remedial action I’ve taken is effective?

The benefit of machine learning and accurate forecasting is that these predictions and problem area identification will take place very early in the billing cycle (or reporting period), allowing for sufficient time for any remedial action to make an impact. And if you continually feed your machine learning algorithm with updated information (data), your predictions will update dynamically, and increase in strength.

If you’re interested in using data analytics to optimise your collection performance, download our expert guide. You can also read more on our predictions tool, Prosperity.

Increase your collection and recovery yields with data analytics

Perry de Jager
Perry de Jager
Perry has been involved in Collections and Recoveries for the past 12 years, spending time in different market segments ranging from law firms to investment companies. At Principa, Perry has worked on extended projects within both South Africa and the Middle East with some of the largest financial organisation, providing on-site consulting within the collections and recoveries space covering strategy, process, people and technology.

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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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