Can prediction markets fix a multi-billion-dollar information gap for African startups?

Consider a hypothetical but common scenario in Africa’s tech ecosystem: A fintech startup raises $35 million in 2023 to bring embedded finance to SMEs across East Africa. The thesis was elegant: small businesses needed working capital, banks weren’t serving them, and mobile money made digital lending feasible. Eighteen months later, they’d burned through the capital and were pivoting.

The problem wasn’t execution. It was information, but not the kind you get from customer surveys. They needed to know what price would actually stick once competitors showed up, once regulations clarified, once the operational mess of real lending set in. 

Pre-launch research suggested 8%. The market reality was 4%. That gap cost $35 million. The knowledge existed scattered across failed pilots from dozens of operators who’d already learned this lesson, but there was no mechanism to aggregate it. No market where someone who’d tried and failed could stake capital on “SME lending won’t work at 8%” and get paid for being right. So the mistake gets repeated, as it does every quarter across African tech.

A multi-billion-dollar information problem

Africa’s tech ecosystem raised $4.1 billion in 2025. Accelerators are everywhere. Infrastructure has improved dramatically. The ecosystem doesn’t have a talent problem anymore; it has an information problem.

Stories like these repeat every quarter: A Nairobi-based logistics company burns $8 million after discovering delivery costs were 3x their projections. A Cape Town SaaS startup spends two years building before learning that enterprises won’t pay their pricing. A Lagos marketplace dies because smartphone penetration assumptions were wrong by 40 percentage points. 

As of the most recent data of 2020, 54% of African startups fail, which amounts to roughly $2.2 billion in destroyed capital from 2025’s funding alone, not counting opportunity costs or damage to the ecosystem’s credibility.

Western ecosystems have decades of benchmark data through Gartner, Crunchbase, and PitchBook. African founders get $40,000 consultant decks that are outdated before delivery, confidently declaring Total Addressable Market (TAM) based on extrapolating Indian growth rates. How much waste could be avoided if founders could validate assumptions before building, VCs could price risk based on ecosystem consensus, and operators could check beliefs against people who’d already tried?

The behavioral and technical foundation already exists

The infrastructure for prediction markets doesn’t need to be built from scratch. The betting industry has already solved the same challenges that make prediction markets valuable for information discovery in Africa.

Sports betting generated $3 billion in 2025 across the continent. Data from GeoPoll’s 2025 Betting in Africa survey reveals that a large percentage of Africans already have experience using betting platforms. Nigeria leads the way with 168.7 million bettors, followed by Kenya with 58.3 million and South Africa with 45.5 million. Platforms like Bet9ja, SportyBet, and Betano dominate the markets across Nigeria, Kenya, and South Africa, while systems like M-Pesa in Kenya and Opay in Nigeria make it easier for bettors to fund their wallets instantly on their mobile phones.  

This represents millions who are already comfortable with the mechanics: reading odds, staking capital through mobile interfaces, waiting for outcomes to settle. They calculate expected value, compare markets, and make risk-adjusted decisions from their phones. Mobile money penetration has created a user base that can stake real capital on uncertain outcomes through digital interfaces.

What’s missing is direction. Those billions flow into “Who wins the Premier League game?” while critical questions go unpriced: Will regulators approve embedded lending in Kenya within 12 months? Will Nigerian enterprises pay more than $200 per month for B2B SaaS? Will Accra’s last-mile delivery costs fall below $2 by 2026?

The mechanics are identical: assess probability, stake capital, wait for resolution. Only the subject matter changes.

A founder sees 41% odds that Nigerian enterprises will pay $200+ monthly for B2B SaaS and pivots to a cheaper model before building. A VC sees 67% odds that Flutterwave will face serious competition within 18 months and recalibrates portfolio construction. An operator watches Kenya Startup Act odds drop from 55% to 34% in two weeks and adjusts hiring plans accordingly.

Markets aggregate distributed knowledge into interpretable signals. Eventually, people stop asking “How much can I win?” and start asking “What does the market think?” That’s when betting becomes infrastructure.

What’s at stake

Building a prediction market means competing with a $3 billion sports betting industry that has network effects, dopamine loops, and cultural entrenchment. Sports betting offers instant gratification. Prediction markets offer delayed resolution, require domain expertise, and resolve on timelines measured in months.

The strategy isn’t to convert casual sports bettors. It’s to target professional decision-makers: founders validating assumptions before building, VCs pricing risk on emerging sectors, operators tracking regulatory shifts, journalists seeking data-driven insights.

An efficient prediction market gives African tech a mechanism to validate assumptions before capital deployment and aggregate distributed knowledge into actionable signals. Failure rates drop at the margins, especially on pricing misjudgments, regulatory timing, and demand validation. The system learns faster because knowledge compounds rather than fragmenting across failed experiments. Without this, platforms built elsewhere will aggregate African market intelligence and capture the data moat.

Somewhere, a fintech founder is realising they have the mobile money rails and regulatory relationships to launch this. Somewhere, a venture studio sees that the behavioral foundation already exists.

One will move first. The question is whether they’ll be African.

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Edike Joseph is a product manager and fintech researcher focused on payments infrastructure, prediction markets, and how technology can reduce uncertainty in African economies.