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Principa's Top 10 Data Analytics Blog Posts For 2016

January 5, 2017 at 4:33 PM

We take pride in our ability to predict - from the results of the 2015 Rugby World Cup and the 2016 Oscars to predicting profitable customers and customer churn. However, there is no denying that 2016 was a year full of shocking, unexpected events - from Brexit and the US election results to the acrimonious break-up of "Brangelina" (shocking!) and the sad loss of some very talented artists.

Whether you saw 2016 as a good or bad year, it's now behind us as we begin this month with optimism and great anticipation of what 2017 holds for us. But before we let go of 2016 completely and "wipe away the tears, pick up the pieces and move on," we've had a look at which of our data analytics blog posts generated the most interest in 2016 and listed them below by order of popularity in case you missed any of them:

10. The Top Predictive Analytics Pitfalls to avoid

Predictive models are not bullet proof. The commoditising of Machine Learning is making data science a lot more accessible to the non data scientists of the world than ever before. With this in mind, my colleague and I sat and pondered, and we devised the following list of top predictive analytics pitfalls to avoid in order to keep your models performing as expected. Read more...

9. Using Big Data Analytics to prevent crimes the "Minority Report" way

It’s been almost 15 years since we saw the future of crime prevention in “Minority Report” – but today, we are beginning to see those then fictitious yet fantastical methods of predicting and preventing crime being implemented in various parts of the world. I’ll briefly mention three examples below of how analytics is already being used to prevent crime today before going into more detail on a fourth example: using analytics to prevent a criminal from re-offending. Read more...

8. How to get started with Machine Learning

The benefits have been recounted many times, but now that Machine Learning has the business world’s attention, how does one get started?  Moving into the machine learning space can be somewhat daunting, but we hope this blog post provides some guidance that you will find helpful. Read more...  

7. How mobile and social data are changing the face of Credit Scoring

Thanks to the prevalent usage of mobile phones and social networks in developing markets, fresh – albeit non-conventional - sources of consumer data are available in abundance for financial concerns to tap in to, and some companies aren’t waiting to get left behind in the race to be the first to shake hands with this new customer. Read more...

6. 3 Ways Credit Risk Managers should be using Big Data

In order to survive and thrive in this economic climate, credit risk professionals need to consider innovative means of decreasing default rates and improving the accuracy with which credit is issued. One such way is applying data analytics to Big Data. Read more...

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5. You want fries with that? Using machine learning to cross-sell and up-sell

Today, the generation and tracking of customer data, transaction data and purchase behaviour data are enabling companies to move away from a generic upsell and cross-sell to a personalised one, and machine learning is ensuring data-driven recommendations reach the right customer at the right time. Read more...

4. What is Machine Learning?

Here's a blog post covering some of the most frequently asked questions we get on Machine Learning and Artificial Intelligence, or Cognitive Computing. We start off with "What is Machine Learing?" and finish off with addressing some of the fears and misconceptions of Artificial Intelligence. Read more...

3. Data Scientists Predict Oscar Winners

Following a highly successful initiative of using Machine Learning to predict last year’s Rugby World Cup results, we're trying our hand again at predicting the future and revealing some interesting insights along the way about another major event: The Academy Awards, or the Oscars. Read more...

2. How Machine Learning is helping Call Centres improve Customer Experience

The call centre world, unsurprisingly, ranks as one of the highest adopters of data analytics platforms year on year. This is largely due to the invaluable insights we gain through the analysis of thousands of calls received each day by the typical call centre.  With speed being of the essence in making the right decision at the right time for each caller many call centres are turning to machine learning to automate their data analysis and make crucial customer experience decisions within seconds. Read more...

1. How Marketers use Machine Learning in Retail

When trends and insights are used to develop a campaign or an entire marketing strategy, there’s considerably less guesswork and a greater chance of success. To get a better idea of machine learning in practice, let’s have a look at how two of the world’s top retailers are using machine learning to improve marketing ROI. Read more...

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Julian Diaz
Julian Diaz
Julian Diaz was Head of Marketing for Principa until 2017, after which he became Head of Marketing for Honeybee CRM. American born and raised, Julian has worked in the IT industry for over 20 years. Having begun his career at a major software company in Germany, Julian made the move to South Africa in 1998 when he joined Dimension Data and later MWEB (leading South African ISP). Since then, Julian has helped launch various South African technology brands into international markets, including Principa.

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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.

Collections Resilience post COVID-19 - part 2

Principa Decisions (Pty) L

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Principa Decisions (Pty) L