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An AI-driven lending platform backed by one of South Africa’s biggest lenders is expanding into two of Africa’s largest credit-starved markets, its CEO told Semafor, betting its machine-learning algorithms can maintain a default rate that is a fraction of traditional commercial banks.
Optasia, which made its stock market debut last year, uses AI and mobile data to provide micro loans, working capital, and airtime advances to unbanked consumers and small businesses. It offers a rare window into how capital flows through Africa’s vast informal economy, exposing a blind spot in standard economic reporting on issues such as poverty, employment, and gross domestic product.
The firm facilitated about $6 billion in credit across 38 markets in developing nations last year, and is now looking to push into Egypt and Ethiopia, CEO Salvador Anglada told Semafor. Banking credit to the private sector is among the lowest globally, standing at under 10% of GDP in Ethiopia because decades of state-led financing channeled money almost exclusively toward public infrastructure. In 2024, Addis Ababa opened its banking sector to foreigners for the first time in 50 years, aiming to inject international capital into a historically closed and credit-starved market of 100 million people.
Egypt’s banking credit to the private sector stands at 30% as lenders direct capital to government debt and blue-chip corporates, leaving the proportion of loans to deposits unusually low at just over 50%, according to World Bank data.
The expansion, which is in its early stages, comes as Optasia posts a group-wide default rate of 1.2%, or around $60-70 million, a metric Anglada expects will hold steady despite scaling into high-density economies with a combined population exceeding 200 million.
That loss ratio makes the business model viable where traditional banks have failed, he said. “Our borrowers maintain these micro-loans almost like a vital utility. They know that by repaying on time, they maintain access to liquidity and build a positive credit profile for the future.”
Traditional banks in sub-Saharan Africa running unsecured lending portfolios routinely write off 10% to 15% of their loans as bad debts.
South Africa’s Standard Bank, Kenya’s Equity Bank and Nigeria’s Access Bank are, by assets and value, the largest of the continent’s traditional banks.
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Launched 14 years ago as a tool to sell on credit, direct cash and digital loans now account for 75% of Optasia’s transaction volumes. Optasia runs roughly tens of millions of daily credit decisions through a stack of 200-plus machine-learning models that draw on thousands of alternative signals per user.
Traditional banks cannot profitably process a $20 or $50 micro advance because “they are not coming from the digital world and they know how to create these complex algorithms”, Anglada said, adding that its AI scoring allows his company to serve customers with no formal credit history at risk levels lower than many retail banks. He said that about a third of total credit volumes is drawn by informal enterprises — street vendors and local shopkeepers.
South Africa’s FirstRand holds an anchor equity stake in Optasia, giving the lender exposure to the platform’s 400 million user base without expanding its branch network or taking on manual “know-your-customer” overheads. Anglada said FirstRand views the company as a specialized technology and platform provider that enables access to the base of the pyramid in ways a traditional bank cannot replicate. FirstRand, with a 26% stake, is the second-largest shareholder behind private equity outfit Chronos Capital with 30%.
Notable
- Former Kenyan ICT Minister Joe Mucheru breaks down how machine-learning credit scoring is driving the growth of micro loans in sub-Saharan Africa, saying data from the fintech platform JUMO shows over 50% of informal micro enterprises receiving algorithmic loans hire at least one additional employee.