How Banks Can Avert Disruption with Data Analytics and AI

Over the last decade, the number of mobile money providers and lending platforms in African markets has surged. As smartphone penetration rises and data costs decrease, a growing number of Africans are switching to digital banking. Last December, three digital banks announced their launch in South Africa, the continent’s most advanced banking sector. Their lower cost structure and aggressive customer acquisition plans will help to capture a larger share of the banking market but, contrary to doomsday predictions that traditional banks will be disrupted and forced out of the market, they do not pose an existential threat to traditional banks.

With their scale and legacy, incumbent commercial lenders  still dominate the banking sector, but they shouldn’t be complacent when faced with technology-driven changes in the industry. To compete with up-and-coming digital banks, traditional banks should turn to advanced data analytics which uses algorithms  to analyze large data sets and draw actionable insights. Here are the 3 reasons why brick-and-mortar banks should employ data analytics.

1.Win Underbanked with Innovation

Traditional financial service providers (FSPs) can use data analytics to acquire new customers, especially among the unbanked population. Over the last decade, sub-Saharan Africa has made progress in closing the financial inclusion gap — with financial inclusion rates increasing from 23% in 2011 to 43% in 2017 — largely due to surging mobile money adoption. While mobile money has focused largely on transfers and payments, the next wave of fintech platforms has expanded into lending.

Fintech startups such as Branch, Paylater and Bank-MNO offerings like M-Shwari and Fuliza, analyze alternative data sets, such as airtime top up and bill pay, to determine credit risk and lend to unbanked customers. For example, Branch’s app crunches all types of data on users’ phones, ranging from bill payments to bank balance receipts, to generate credit scores. Banks can also use data to compete for the huge pool of unbanked customers by partnering with third-party data analytics providers and building new data partnerships. For example, SuperFluid Labs’ data analytics offerings includes SuperScore, which empowers lenders to manage life-cycle credit risk by building credit scores for customers from multiple data sets using the power of Artificial Intelligence (AI) and new alternative data sources.

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Moreover, SuperScore provides predictions on the likelihood of default by customers by analyzing past transactional and behavioural data through proprietary algorithms.  Across the continent, banks struggle with a high rate of non-performing loans (NPLs). For example, in Ghana, over one-fifth of total loans are NPLs while Kenya has a 10 percent NPL rate, as compared to the global average of 3-4 percent. Using data-driven risk assessment tools, credit officers can expand their loan books, increase revenues from interest payments, improve recoveries and hedge risks.

  1. Upsell and Cross Sell Products

Data analytics can also help banks to optimize sales from existing customers, leading to opportunities to upsell and cross-sell new products and services. The global banking industry could increase earnings as much as $1 trillion annually with one-fifth resulting from informed pricing and promotion strategies. Fintech companies in sub-Saharan Africa have access to rich customer insights insights — age, gender, online behavior, purchasing power, and geographic location — which are a gold mine as they enable companies to develop tailored products addressing pain points of users. Banks should follow their lead, using these insights to offer new products to customers. At SuperFluid Labs, after analyzing customer data for a banking client, we observed that a large percentage of customers, clearly cash-strapped, were regularly drawing down their savings accounts. We, therefore, proposed that the bank offer a new credit product, using the savings account as collateral.

  1. Develop Powerful Partnerships

With their increasing push into markets and sometimes explosive growth, African fintech startups can seem like a mortal enemy to FSPs. But, this viewpoint is more fear than reality. Fintechs and traditional banks have mutual interests in working together. Data analytics is the crux of a successful partnership, bridging the differences between the two types of companies. While the global financial services industry is increasing spending on IT infrastructure, many African banks, facing macro-economic headwinds and hamstrung with legacy technologies, often struggle to maintain pace with rapidly changing customer expectations.

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In contrast, fintech startups are early technology adopters and are much faster to experiment and innovate, giving them a first-mover advantage. In turn, as incumbent players, traditional banks can provide market access, brand credibility, guidance and improved relations with regulators to fintechs.

Banks and fintechs can combine their natural strengths to pursue historically underserved customer segments, such as small and medium enterprises (SMEs), and retain existing customers through improved customer experiences. Third-party data providers play a critical role in ensuring successful partnerships between banks and fintechs. Not only do they offset limited digital and data expertise and in-house talent shortage, they can help bridge a potential culture gap between banks and fintechs by fostering a data-first ethos.

Sub-Saharan Africa’s financial services industry’s underdeveloped electronic transaction infrastructure was its Achilles heel and led to the entry of global fintech companies. The lack of data analytics capabilities should not be another reason why local financial institutions fail to adequately serve African customers. Using data appropriately and smartly, banks can unlock new growth by investing in innovative data analytics solutions and platforms and upskilling existing staff, and thus avoid losing their market to new disruptive players. Higher customer acquisition and retention, lower NPL rates, and upselling and cross-selling opportunities — these benefits are just the beginning of what data can yield for banks and most importantly for underserved customers. Moreover, competitors can also be collaborators with data analytics providing the fertile ground for partnerships between banks and fintechs. Bill Gates famously said,  ‘Banking Is necessary; banks are not’. With the help of data analytics, banks can maintain dominance in the provision of essential financial services.

Timothy Kotin is the  CEO of  Superfluid Labs, which helps businesses harness untapped potential with predictive data analytics, business intelligence and dynamic customer insights. Its data analytics platform provides credit scores and more effective business intelligence through Big Data and artificial intelligence.



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