The model, which analyzes vocal tone, speech content, and visual cues during digital interviews, targets individuals lacking formal financial documentation, offering loans between ₦30,000 and ₦50,000 (approximately GH₵265–GH₵441).

Currently achieving 76% accuracy in pilot phases, the AI simplifies traditional lending criteria by focusing on two factors: repayment capacity and intent. Applicants respond to basic questions about income and repayment plans via voice or video, bypassing paperwork-heavy processes. “The goal is to help vulnerable groups prove their reliability without traditional collateral or credit history,” said Lendsqr CEO Adedeji Olowe.

The initiative addresses stark gaps in Nigeria’s credit landscape, where only 6% of adults and 12% of small businesses access formal loans despite high demand. Existing fintech solutions often rely on costly verification methods, inflating borrowing costs. Lendsqr’s approach could reduce overheads, potentially lowering interest rates and expanding financial inclusion.

Supported by Nigeria’s Ministry of Communications, Innovation & Digital Economy and Google, the company plans to publish its methodology by late 2025 for broader industry adoption. Pilots will also launch in Canada, targeting immigrants and students excluded by conventional credit systems.

While the tool’s potential to transform microlending is significant, challenges remain. Privacy concerns over biometric data collection and the need to boost accuracy to 90% before public release are critical hurdles. Olowe emphasized the model is tailored for small, impactful loans rather than large-scale financing, aligning with efforts to bridge Africa’s $330 billion credit gap for underserved communities.

The project reflects a growing trend among African fintechs to leverage AI for financial inclusion, though ethical debates persist around data security and algorithmic bias. If successful, Lendsqr’s innovation could set a precedent for balancing technological advancement with equitable access, reshaping credit assessment in markets where trust hinges on transparency.