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

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Machine Learning Is Placing Risk Managers On Fraud's Front Lines

April 27, 2016 at 2:46 PM

Few take on a larger portion of the responsibility to steer their organisations to success than Risk Managers. And with fast-moving consumers, a globalised marketplace, unabated industry disruptions and shifts seemingly all occurring in unison, modern Risk Managers face a new set of challenges to that of their predecessors. But now, banks and other financial institutions are using historical customer transactional data to detect unusual activity on buyers' debit and credit cards to freeze transactions until purchases can be verified by the card owners.

Also, identifying common characteristics of insurance fraud through machine learning is reducing the manpower required to investigate claims and minimising risk in the process. And considering that across-the-line fraud in the US alone is estimated at around $80 billion per annum, the need for a proactive stance against this criminal activity is quite apparent.

Separating friend from foe with automated decision-making

Machine learning algorithms applied to insurance claim data are enabling insurance companies to automate fraud detection. For customers with legitimate cases, this significantly shortens turnaround times as above-board claims can then be processed in much shorter time spans, thus increasing customer satisfaction by speeding up the payment of legitimate claims.

Here is an insightful whitepaper on how data analytics is automating the detection of fraud and giving businesses a stronger foothold in the ongoing battle against fraud.

By actively learning card owners' buying patterns  and building consumer profiles based on this information, predictive models are making near real-time decisions on debit card transactions that may or may not be fraudulent. This enables risk management teams to take a more preventative stance in their ongoing combat against fraud. Considering the vast amounts of transactions that occur globally every minute, it's simply impossible for humans to analyse every transaction, highlighting the unique advantage that machine learning provides risk teams in preventing fraudulent activity on consumers' debit cards.

Here are 3 ways Risk Managers should be using Big Data.

As machine learning algorithms continue to learn how to distinguish normal transaction parameters from abnormal ones, companies across the board will become better able to prevent fraud instead of recovering from its aftermath.

Shifting from a reactive to a proactive stance

For years, companies were content to look at their data through a retrospective lens, but advances in technology has shifted the current way of thinking. Machine Learning is a way to help shift our focus from what happened to what is most likely to happen, and in so doing, empowers us to take the right pre-emptive or corrective action that serves the need of the business. From the world of sport, to business operations, to product development, and even the newspaper industry, predictive analytics is making its impact felt far and wide.

For more information on how Principa's data analytics platforms are helping business across Africa leverage untapped value in their data assets, contact us to learn more about how we make data work wonders.

Using machine learning in business - download guide

Blog post originally published 13 January 2016.

Image credit: http://www.forexautotradingrobots.com//

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.

Latest Posts

2021: Pursuing the pockets of profit

During the COVID-19 crisis, the media has focused much on the weak economy and stressed South African consumers. Figures show an increase in unemployment and for those lucky to be employed, many suffered decreased earnings through salary cuts. All this points to a highly strained economic environment.

Are we entering a mortgage provision spiral?

The South African credit bureau TransUnion recently released data on the performance of various different products within the bureau in their ”Quarterly Overview of Consumer Credit Trends” for the third quarter of 2020. With the COVID-19 crisis, 2020 was characterised by a severe reduction in account originations and payment holidays in Q2 with a high increase in non-performing accounts in Q3 as payment holidays ceased and stressed consumers failed to pay their accounts. The table below illustrates how each product showed worse performance (in terms of accounts moving to 3 months or more delinquent) year-on-year in Q3. For more on how Principa can assist your business in credit scoring and IFRS9 Provision click here and here. The table typically follows payment hierarchical patterns with credit cards performing best, but also illustrates risk-appetite for each product with clothing, microloans and retail instalments all showing the worst performance. For the retailers the closure of stores in Q2 meant fewer new good accounts were washing through, so the bad performing books in Q3 are/were accentuated. What does stand out, however, is the performance of mortgages that suffered a 350-basis point slump year-on-year in Q3. This is off a low overall “bad rate” too. Will the mortgage books bounce back, or will we see ourselves enter a mortgage provision spiral as we did in 2008/09? “When the spiral begins the knock-on effects can be catastrophic with provisions taking a hard hit.” Provisions in mortgages are unlike other product classes in consumer credit. When the spiral begins the knock-on effects can be catastrophic with provisions taking a hard hit. Banks around the world valued their books very differently post 2009 compared to pre-2008. A certain South African retail bank’s mortgage book valuation dropped by over 90% due to the knock-on effect of a mortgage provision spiral. Now the property market has been subdued for some years (compared to the bullish period leading up to 2008) so we are not expecting a mortgage crisis, but it is possible that a spiral will affect mortgages significantly as we enter a bearish market. How does the spiral work? An increase in defaults loans will mean the banks will need to make a difficult choice on whether to show leniency on the defaulting customers or to take strong action with repossession being the ultimate act. An increase in defaults also typically means that the book is not aging as expected and that the Probability of Defaults (PDs) experienced are higher than expected. Increase in defaults typically leads to more repossessions. More repossessions will mean the bank is left with an increased amount of stock (properties) to sell. More stock will likely mean bigger haircuts (i.e. difference between the net selling price of the property and its value) as the market becomes a buyer’s market. More stock together with the fact that banks will tighten lending criteria, will push property prices down. Bigger haircuts will mean an increase in shortfalls (i.e. where the net-value received for a property is less than the outstanding balance of the mortgage). More shortfalls will mean fewer voluntary sales to avoid defaulting (in bullish markets, consumers in financial destress may be pushed to sell their property; they’d likely make a profit from the property thus incurring no shortfall). Lower house prices will also contribute to more shortfalls and this in-turn results in much higher loss-given-defaults (LGDs). Higher PDs and LGDs pushes up provisions dramatically. Fewer voluntary sales to avoid defaulting means more accounts will now default and the spiral continues. The difference between a bullish and bearish market is illustrated in the image below. Whether we enter a bearish market and endure a mortgage spiral will depend on defaults increasing (generally due to the stressed South African economy) and whether banks enforce an increased number of repossessions. Whether we enter a bearish market and endure a mortgage spiral will depend on defaults increasing (generally due to the stressed South African economy) and whether banks enforce an increased number of repossessions.   Performance bounce back for the retailers At Principa we work closely with many retailers and we are aware that for many of them, Q3 saw accounts accelerate to a 3+ arrears state, but thereafter the book improved somewhat (i.e. those who were already stressed – accelerated to default – an inevitable ultimate state for some. On the other hand, the survivors are those resilient to the economic woes and continued to perform well; new accounts are also open). We look forward to establishing whether the same is true for mortgages when the performance figures are released for Q4. For more on how Principa can assist your business in credit scoring and IFRS9 Provision click on the links here and here or email us at info@principa.co.za.

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.