It’s no secret that South African banks and financial services providers draw on massive volumes of data to make decisions and provide targeted services to middle and upper income earners. When assessing individual credit applications, for example, banks will draw on highly detailed customer profiles and many years of data collection. However, in the midst of a global health crisis and widespread economic shutdowns, many traditional data sources for credit decision-making have become far less reliable. Extended national lockdowns have significantly reduced data flows from traditional data sources, and in many instances, have rendered them less useful (as many households begin to default on debt repayments and fall behind).
Even when putting the devastating impact of the pandemic aside, the majority of the South African population is left out of credit decision-making processes on a daily basis – and as such, cannot receive the same access to credit and other key banking services that higher income earners currently enjoy. In a post-lockdown economy, with severely dampened business activity, these entrenched inequalities within banking are in danger of intensifying.
Arguably, this is primarily because banks don’t have enough data around these potential customers to enable predictions … and thus offer credit under their current models. In the information-hungry world of credit risk and banking, these customers are referred to as ‘thin profile’ – and while they represent opportunity, most financial services providers haven’t been able to solve the conundrum of having scant data on them.
Until now, that is. With the introduction of secure data exchange platforms (powered by artificial intelligence) that are both POPIA and GDPR compliant, savvy financial services providers can attain the ability to tap into alternative data on traditionally excluded customers in South Africa. For example, by partnering with large retailers (such as Clicks or Pick ‘n Pay), banks can engage in secure, anonymised data sharing to obtain information that can drive smart credit decisions – and potentially unlock credit and other financial services for many previously excluded individuals falling within the ‘thin profile’ segment. In essence, by harnessing secure data sharing platforms, banks can now leverage reams of important alternative data sets to drive decision making…both affordably, and at scale.
When presented with a credit application by an individual who falls within the ‘thin profile’ category, a bank could immediately look to other sources of data on this individual – and quickly broker data sharing partnerships where needed. So, in this case, the bank could gain secure access to anonymised mobile telephone accounts – which have proven to be extremely useful when building and assessing credit scores. If the bank finds that this type of alternative data is useful to predicting credit outcomes, then the bank can begin to request consent from consumers to access things like mobile phone accounts, retail accounts, etc.
Importantly, banks can test the efficiency of this approach by analysing the profiles of customers who were previously ‘thin profile’. For example, they can build a data profile on such customers based only on alternative data sets, and then compare this profile to their current one which is based on ‘traditional’ data sets.
While these types of solutions aren’t necessarily new in the financial sphere, what is new is the ability of secure, AI-powered data exchange platforms to automatically anonymise alternative data (and thus keep the process POPIA and GDPR compliant), and drive innovative data sharing partnerships.
These types of solutions not only empower many data owners (by suddenly making their data both valuable and accessible), but they also empower consumers by protecting data privacy while still driving access to key financial services.
For many banks that are still struggling to serve South Africans in an impactful (as well as profitable) manner, the newfound ability to unlock alternative data securely and efficiently can be nothing short of transformative. Arguably, as banks harness new data sets to make decisions around credit, they will quickly discover many other opportunities that have been ‘locked’, or hidden, within alternative data.
By Matt Mckie, co-founder and Chief Commercial Officer at Omnisient