The Data Analytics Blog

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

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Top 10 Data Analytics Blog Posts For 2015

January 5, 2016 at 9:59 AM

 We take a look at the Top Ten blog posts which received the greatest number of views in 2015.

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10. Data Analytics 101: Turning Credit Risk Managers into Information Addicts

In the field of credit risk management, few would challenge data’s role in financial forecasting, lender analysis, credit-modelling and risk aversion. In short, credit risk managers are no strangers to data. But it could be argued that the value and volume of data they have access to largely determine the quality of their decision making. The shift towards more technology and data-centric business models has created new opportunities for those in the credit risk landscape to play more collaborative roles and engage other business divisions to produce positive outcomes in shorter time-spans.

Read the full story here

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9. How Mobile and Social Data are changing the Face of Credit Scoring

In emerging or developing markets where formal credit history collection infrastructure or credit bureaus are lacking, the majority of people without reference data would struggle to achieve anything resembling an impressive credit score. As a large contingent of previously unbanked individuals join their respective middle classes in increasing numbers worldwide, lenders are finding creative ways of profiling and welcoming newcomers to the world of financial services.

Read the full story here

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8. Three Things successful Loyalty Programs are doing right

Simply put, loyalty programs exist for two simple reasons: to motivate increased engagement with your brand and to collect data in order to build deep customer understanding. But they also exist for a third reason: customers want them. In a study by Nielsen, 84% of respondents said they were more likely to choose retailers that offered a loyalty program. Forrester Research have found that 64% of consumers agree that loyalty programs influence where they make purchases, and 50% agree that loyalty programs influence what they buy.

Read the full story here

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7. How Netflix and Data Analytics are making Movie Magic

Kevin Spacey may not have given data analytics the nod at his acceptance speech last year at the Golden Globes, but that doesn’t mean the star doesn’t understand the depth of data’s role in the success of his hit show, House of Cards. Since the early days of its DVD-mailing business model, Netflix has been aware of the potential that lay dormant in consumer data. In 2006, the company offered $1 million to developers to create an algorithm that could best predict how viewers would rate movies based on their previous ratings. With the arrival of video streaming, thanks to internet maturity and consumer buy-in, data became abundant and Netflix were poised to capitalise on it based on the success of their initial foray into analytics. Today, House of Cards is a multi-award-winning TV-series that is conceptualised, cast, produced and written in partnership with the insight gained from massive volumes of consumer data. 

Read the full story here

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6. How Big Data Analysis is driving Business Transformation

Corporate silos may have been necessary in a burgeoning industrial era some decades ago, but times have changed. The era of social, mobile, analytics and cloud (SMAC) technologies has been fuelling a new wave of business transformation. Virtually every industry is being affected by the SMAC phenomenon and we’re currently only scraping the surface of what is possible.

Read the full story here

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5. Four cool ways Data Analytics is changing the World of Sports

After the success which Data Analytics had in predicting the outcome of the Rugby World Cup games, Principa had a look into other ways data analytics is being used in the sports world today. There are indeed many ways, but for the sake of brevity, we looked at four of the more interesting ways that data analytics is changing the world of sports.

Read the full story here

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4. Football had the Octopus, Rugby has Data Science

Many of us remember the hoopla around the predicting ability of the now deceased FIFA World Cup predictor, Paul the Octopus. For those who don’t recall, Paul was an Octopus at a German aquarium that famously predicted with 100% accuracy the results of team Germany’s six matches and final match of the 2010 Soccer World Cup. Although, Goldman Sachs defending their 37.5% success rate back then contested that Paul would have only been 33% accurate had he had to predict the results of all 48 games, including draws. Be that as it may, given the option of breaking into Cape Town Aquarium and kidnapping an octopus to help us predict the outcome of the Rugby World Cup or rather relying on data science and machine learning, we stuck to what we know and believe in and it has paid off.

Read the full story here

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3. Can Machines predict the Outcome of the Rugby World Cup?

To kick off the SuperBru Challenge on predicting the results of the Rugby world cup we explained the methodology and data used to make the predictions for the rugby world cup.

Read the full story here

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2. Out-predicting 99.68% of Humans during the Rugby World Cup

Another favourite from the Rugby World Cup challenge. Half way through the challenge we thought we’d share some of the key lessons learned so far and the challenges we face predicting the results of the up-coming knock-out stage Rugby World Cup matches.

Read the full story here

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1. Using Predictive Analytics to beat the Bookies

It was Man vs. the Machine at the Principa HQ's as our data scientists applied predictive analytics and machine learning to predict the winners and spread of each match during the Rugby World Cup. We signed up two internal teams of data scientists onto sports prediction site SuperBru.com as an exercise to put theory into play in last year’s Rugby World Cup. By applying the same principles used to predict customer behaviour for our financial services and retail clients, our two teams are vying against each other to develop algorithms and predictive models that can predict the outcome of the matches with the highest accuracy.

Read the full story here

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.

Latest Posts

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

Collections Resilience post COVID-19

Principa Decisions (Pty) L