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More effective credit risk scoring for Africa

Advanced data analytics can help banks to increase their competitiveness and reduce their risk exposure.


Johannesburg, 15 Dec 2017
Charles Nyamuzinga, Risk Consultant, SAS South Africa.
Charles Nyamuzinga, Risk Consultant, SAS South Africa.

In tough economic times, one of the first things consumers default on is their credit repayments. As we enter the festive season, there is no doubt an even greater temptation to spend more on credit than usual, with the result that consumers are slipping further into a debt trap that they can't get out of - and something that the banks ultimately take the brunt of.

In an attempt to avoid a repeat of the 2008 financial crisis, financial regulators are thus implementing stricter compliance measures. Apart from the new International Financial Reporting Standards (IFRS 9) accounting standard, banks also need to start preparing for Basel IV, even though they have only just gotten to grips with Basel III. Basel IV revises methodologies for the determination of capital requirements, which means that capital calculations across all risk types will change.

In the case of both requirements, explains Charles Nyamuzinga, Risk Consultant at SAS, regulators are becoming more stringent on governance and control issues around methodologies and data that banks use to develop the credit scoring models, and to determine how much capital they need to cover future losses.

"With Africa's economy under pressure, banks across the continent are facing increasing pressure to better manage their credit books and to properly vet customers before granting them loans. This is as much for their own protection as it is for the consumers'. The only way to do this is by applying advanced analytics to internal and external customer data, so that banks can build statistical models that automatically screen customers for credit worthiness. However, uptake of these types of solutions in parts of Africa has been lagging," he says.

"Come January 2018, banks will have to be ready to comply with IFRS 9, which will require them to not only predict and make provisions for future credit losses, but also to provide a full audit trail of how they came to those calculations. That means that they urgently need to get the data, models, infrastructure and skills in place to avoid the reputational impact of being non-compliant with IFRS 9, which would reflect non-transparency in financial reporting, as well as affect the bank's ability to do business, for example with investors. Credit scoring is important in determining some of the models and parameters used in meeting IFRS 9 requirements."

But, continues Nyamuzinga, many banks in Africa face two major hurdles to compliance. Firstly, they often don't have access to the volumes of data needed to build effective credit scoring models and, if they do, the data is often 'unclean' and scattered across disparate databases that are not integrated.

Secondly, some banks have not invested in the tools and skills needed to build these models. This forces them to outsource model creation, something that has its own disadvantages. These include high costs, late response to market changes and the possibility of ending up with models that are not aligned to their portfolios.

"Nonetheless, credit scoring is now imperative for all banks, and their ability to gauge whether a customer will repay their loans every month or if they are likely to default, will determine whether banks stay on the right side of the regulations."

"Banks face a tricky balancing act - they either issue a high number of loans and face the risk of customers defaulting, or they issue fewer loans, thereby compromising their ability to generate revenue. Now, new regulations are forcing them to balance their risk and return profiles to ensure that they are profitable, while controlling risk within their loan portfolios.

To throw a spanner in the works, competition in the market is getting fierce, with new entrants competing for a shrinking pool of quality customers."

This is why, he adds, it is now imperative to use advanced data analytics and credit risk scoring models, as this enables banks to obtain a single, accurate view of their customers. By continually training and updating the models, banks will be alerted to problem customers, as well as to opportunities to sell new products to existing customers with high credit scores. This gives them a competitive advantage while allowing them to manage their risk exposure.

"Model monitoring is crucial to ensure that they are performing accurately and are using the most recent data. After all, basing decisions on out-dated and inaccurate models could expose banks to risk, by issuing loans to customers who would not normally qualify. This is one risk of outsourcing model development, rather than developing it in-house. By the time the bank implements the outsourced models, they could already be out of date."

Today's advanced data analytics solutions address a number of challenges facing African banks, suggests Nyamuzinga. They are easy to use and allow anyone to experiment with data visualisations and drag-and-drop interfaces; they pull data from disparate sources into one interface - and prepare the data for modelling; and they ensure that models are retrained based on real-time information, reducing risk exposure for banks.

"Banks will also benefit from economies of scale when many segment-specific models need to be built. Further, building a solid, internal skills base also makes it easier for banks to remain consistent in the interpretation of model results and reports, and to use a consistent modelling methodology across the entire range of customer-related scores."

"Until now, many in Africa have been slow to adopt advanced analytics solutions for credit scoring, but with increasingly stringent regulation, they no longer have a choice. The good news is that analytics will help them to both increase their competitiveness and reduce their risk exposure - all without requiring a massive investment in skills, time or infrastructure," he concludes.

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