Case Study: The importance of AI in emerging markets - Outside Insight
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Case Study: The importance of AI in emerging markets

Artificial intelligence and access to external data has the potential to yield particularly significant dividends in Africa by enabling greater financial inclusion

Key takeaway

Superfluid Labs CEO and data evangelist Timothy Kotin explains the significant impact data and artificial intelligence can have in Africa, where data has traditionally been much harder to come by. From fintech to policy to microfinance, the ability to process large volumes of consumer data can have far-reaching impact across the continent.

 

During the Outside Insight book launch in London, author and Meltwater CEO Jorn Lyseggen discussed the impact AI can have on developing markets – particularly in Africa, where the prevalence of consumer data for the first time is enabling access to everything from micro-loans via credit scores, to better healthcare.


Timothy Kotin, CEO and Co-founder of Superfluid Labs, a data analytics firm focused on fostering digital inclusion, writes about the importance of embracing AI in Africa. Here are excerpts from his post, originally published in AllAfrica.

By: Timothy Kotin, Superfluid Labs

Machines might scare policymakers from Brussels to Washington, but artificial intelligence could yield a significant developmental dividend in the developing world. In African markets, the technology behind Alexa and Siri can be harnessed to diagnose illness or address traffic gridlock.

One of the most transformative applications of artificial intelligence (AI) is in financial technology, where global investment has risen 38% over the last year. For the two billion unbanked adults worldwide, machine learning could light a path out of poverty by helping traditional lenders approve loans using hundreds of non-traditional data points. AI has the capacity to add value at the individual, small business, and the large corporate level alike across Africa.

Data is just as valuable [as it is in the West] yet harder to come by in emerging markets, where companies struggle to reach potential customers in the absence of granular insights about them. Across Africa, comprehensive, current, and accessible data about public sentiment could have wide-reaching policy impact as well as more traditional, commercial value. Startups such as Kenya’s mSurvey have stepped in, using mobile phones to build up-to-date profiles of local consumers. To truly unlock Africa’s consumer data, companies on the continent must develop their own AI-driven data marketplaces, incentivizing customers to opt-in and share their consumption habits.

Leveraging external data for financial inclusion

Across emerging markets, SMEs have debunked the myth that it is risky to extend credit to the poor. For three decades, Grameen Bank has amassed troves of data on who defaults and lent billions of dollars of microcredit with default rate of less than 1 percent.

To assess creditworthiness, traditional banks in Africa require the detailed financial statements and conventional data points that those in developed economies do. This gives the world’s 800 million small businesses little chance to gain access to credit. Enter machine learning, which collects and analyzes hundreds or thousands of pieces of data in minutes compared with the dozens of traditional data points banks look for.

In 2012, Commercial Bank of Africa and Safaricom partnered to launch M-Shwari which offered digital banking and short-term credit services tailored to the poor in Kenya. In just 5 short years, the service has grown to 20 million customers and almost a billion dollars in micro-loans disbursed, supported by automated credit assessments and instantaneous approval decisions.

Faster, cheaper, computing power has helped lenders leverage more information from large, complex data sets as well as alternative data points such as social media postings, mobile money transactions, and utility bill repayment. Using personalized scoring and risk management, alternative lenders can better predict a borrower’s prudence and stability, lend to clients with a higher risk profile, and still boast lower default rates than traditional banks issuing securitized loans.

Companies like OneFi and Tala have empowered small business owners in African markets, extending credit by placing a value on data such as time spent reading the application’s terms and conditions, the number of moves in recent years, and rent as a percentage of income. With better predictive models tapping into the alternative data that does exist, AI is helping small business owners circumvent the traditional data that does not.

 

Alternative data points evaluated by lenders in predictive models

Social media posts

Mobile money transactions

Utility bill repayment

Time spent reading T&Cs

# of moves

Rent as % of household income

How AI can power job creation in Africa

AI is powerful at the corporate level because small business is big business. SMEs are engines for global job creation, with new businesses generating twice a many jobs as incumbents. In Africa, young companies with less than 20 employees create the most formal sector jobs. Because African markets also boast the highest share of adult business owners, extending credit to more of them means more small businesses can scale and contribute to GDP. Lenders reluctant to lend to SMEs will find themselves edged out of the industry over time by tech-driven upstarts with more inclusive credit scoring criteria. Africa’s population is growing faster than jobs created. To address the gap, we must adopt innovative technology to generate access to the finance, the key constraint for SME growth across the region.

Artificial intelligence is not a tool to be feared, but one to be welcomed and wielded by individuals, small businesses, and corporates. The result is an economic trickle-up effect whereby individuals gain by monetizing their data, marketers can develop comprehensive consumer insights, lenders can tap into new customer segments, and markets grow through job creation and more efficient use of resources. Technology may not be a cure-all, if emerging markets are ever to finally ‘emerge’, the catalytic power of non-traditional predictive models must be brought out of the shadows to the fore.

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