How data can help financial firms drive resilient growth

Financial institutions can use data to maintain stable growth. PHOTO | SHUTTERSTOCK

By KIM KARIUKI
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Summary

• Geo-spatial data can help financial institutions generate operational efficiencies when it comes to identifying high potential markets as part of business expansion, outreach or customer acquisition.
• Understanding the livelihoods and lifestyles of their current and potential customers beyond age, gender and wealth, allows institutions to design relevant products for all clusters of clients.

This month, two new reports painted a grim picture of the state of the country’s financial sector. The first is by the Central Bank of Kenya, which found that profitability of banks had fallen by about 30 percent in the first six months of the year.

The second is by the International Monetary Fund, which showed that microfinanciers in 2019 had lost nearly 50 percent of their deposit accounts and 15 percent of their borrowers compared to the previous year.

For banks and mobile network operators, emergency measures put in place by CBK in March to cushion Kenyans from economic turmoil, have had significant effects with reference to the balance sheets.

Two of Kenya’s largest banks KCB and Equity reported profit reductions of 43 percent and 14 percent respectively in the nine months ending September as compared to last year – largely driven by increased loan-loss provisions and reductions in non-interest income.

Similarly, zero-rating of mobile money transactions led to a 14.5 percent reduction in M-Pesa revenue in the six months ending September this year. It is not surprising, therefore, that banks and Safaricom have sought to engage the lender of the last resort to avoid an extension of the zero-rated pricing regime as was witnessed in June.

These findings point to the fact that the sector is ailing under a disruptive cocktail of competitive market forces, depressed economic activity and regulatory pressure.

Beyond the measures, the difficult business environment occasioned by Covid-19 scourge has driven some banks to consider layoffs, this being evidenced by the recent moves by NCBA and Standard Chartered.

According to a 2017 report by the apex lender, issues plaguing Kenya’s microfinance can be traced back to the rise of FinTech’s and financing costs coupled with heightened credit risk as well as increased competition from digital lenders.

This has pushed industry players into loss-making territory, serving repetitive blows to an industry that was once at the heart of driving financial inclusion in the country.

Undoubtedly, there is need to build resilience in the banking sector. Although the path ahead is largely unforged, leveraging primary and secondary sources can help lower costs, identify untapped opportunities and recover lost ground. For example, geo-spatial data can help financial institutions generate operational efficiencies when it comes to identifying high potential markets as part of business expansion, outreach or customer acquisition.

In addition, it is possible to use advanced analytical methods to segment clients based on important business parameters, including savings rates and loan repayment.

When combined with primary data collection, it is possible to build personas that holistically explain (and even forecast) what customers are likely to do in future.

This can be beneficial in optimising efforts around marketing, product development, credit administration and customer retention. The human account – which unlocks the stories of underserved customers in six countries, including Kenya, India, Myanmar, Nigeria, Pakistan and Tanzania is a notable example of how segmentation supported by qualitative and quantitative data reveals opportunities for financial institutions.

Understanding the livelihoods and lifestyles of their current and potential customers beyond age, gender and wealth, allows institutions to design relevant products for all clusters of clients.

When combined with machine learning, it is possible to push the boundaries even further. For example, by leveraging multiple real-time data sources, including transactional, phone and social media data, it’s possible to train algorithms that have better predictive capability than existing credit scoring models.

In addition, it’s also possible to understand complex behaviour such as what drives loan stacking – a situation in which individuals have multiple loans and sometimes across several providers.

For any country, a resilient financial system is the bedrock for sustainable economic growth given the critical financial intermediation role that they play.

Kim Kariuki is a director, integrated projects at Dalberg Research.