Artificial Intelligence (AI) is helping increase access to financial services in Africa.
In recent years, advances in machine learning, a type of artificial intelligence, have had a profound impact on the delivery of financial services, helping to democratize access in emerging economies in Africa.
For example, it is used to offer loans and credit opportunities to people who might be excluded from the financial system.
AI companies such as FinTech based in Dubai optasia They use machine learning in their credit decision engines to automatically approve microloan applications, which helps expand access to credit.
While Optasia’s technology is not a lender itself, it has been integrated into the lending process, enabling banks and other FinTech firms to automatically assess non-payment risk, resulting in faster decision-making and more intuitive lending products.
In one recent partnership, Optasia Cooperated with Ecobank and MTN To provide micro-loans to MTN customers in Guinea. With capital provided by Ecobank and disbursement handled by MTN mobile money, Optasia’s AI platform provides the critical risk assessment that facilitates loans.
Machine learning also allows lenders to deploy more diverse data sets into their decision-making processes. Unlike traditional credit scoring methodologies that require electronic transaction data to build a credit profile, a generation of African innovators like Optasia are taking advantage of alternative datasets to establish the probability that a particular borrower will default on their payments.
And because telecom companies like MTN have access to a wealth of data on African consumers, they have been at the forefront of alternative credit rating innovation.
Still in its early days, the field took off in the mid-2010s with the incorporation of AI tools into Safaricom’s M-Shwari mobile credit services. Like the recent MTN-Optasia partnership, M-Shwari allows Kenyan Safaricom customers to access microloans, which are disbursed via M-Pesa mobile money with automated loan decisions thanks to artificial intelligence.
With the concept taking root, startups developing tools that use mobile networks and other alternative data sources have popped up across the region in recent years to help make lending decisions.
For example, Cape Town-based FinTech Jomo It uses machine learning to build accurate credit scores and targeted financial products for people without formal financial identification, collateral, or credit history.
Enabling cash-based businesses
An alternative credit rating system goes beyond small consumer loans and can be especially beneficial to small businesses. This is because, in many emerging markets, small businesses suffer from the same thin credit profiles as consumers due to the cash-based nature of such economies.
One African company using alternative data sources to extend credit to previously underserved businesses is Numidawhich caters specifically to traders in the informal and semi-official market.
As Co-Founder and CEO of Ugandan FinTech, Mina Shahid, told PYMNTS in an interviewNumida has built a credit scoring model that doesn’t require electronic transaction data like most people do. Instead, loan applications are processed based on inputs to the mobile app.
“Our claim to fame really is that we built the filing model and all the operating and underwriting practices so that we could offer an unsecured working capital loan to a cash-based company with no digital transaction history,” he noted.
According to Shahid, this differs from other digital lending platforms on the continent because it does not require companies to use point-of-sale systems or engage in the e-commerce market to build a credit score.
Rather than relying on digital transaction data, the company’s property registration model relies on historical data from past loans issued, which seems to make the company’s lending model an ideal candidate for making decisions that are automated, or at least more automated, using machine learning.
However, FinTech still has human credit officers who manage the accounts and collect additional information needed for the underwriting process. But AI does not have to completely replace humans in this process in order to be useful.
What’s more, as AI models become more accurate the more data they are fed with, as Nomeda’s business grows, it will be able to automate decision-making more efficiently, enabling fewer human workers to process more loans.
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