Why Data as Collateral, Not Land, Should Be the New Collateral for Nigeria’s SMEs

Data as collateral is the rallying cry of Sparkle MFB’s chief executive, Uzoma Dozie, as he pushes Nigerian banks to rethink the way they assess risk for small‑business borrowers. In a candid interview with TechCabal on 5 November 2026, Dozie argued that the traditional reliance on physical assets—land, equipment, or personal guarantees—has left a massive segment of the SME sector under‑served. By harnessing transaction data and artificial intelligence, Sparkle aims to widen credit access, lower borrowing costs, and move Nigeria closer to true financial inclusion.

Why data as collateral is a blind spot

For decades, Nigerian banks have required borrowers to pledge tangible assets as security. While this approach protects lenders, it also creates a barrier for the majority of SMEs that operate from modest storefronts or run mobile businesses without owning land. According to the Central Bank of Nigeria’s 2025 financial inclusion report, over 70 % of micro‑enterprises lack sufficient collateral, resulting in a credit gap estimated at ₦3.2 trillion.

Dozie points out that this model is increasingly out of step with a digital‑first economy. “When a trader can show a month‑long stream of sales on a mobile POS, or a farmer can upload satellite‑verified yield data, why should a bank still ask for a plot of land?” he asks. The answer, he believes, lies in treating data itself as a reliable form of security.

Turning transaction data into a credit asset

Sparkle’s platform aggregates real‑time data from point‑of‑sale (POS) systems, mobile money wallets, and e‑commerce dashboards. Using machine‑learning algorithms, the system builds a dynamic credit profile that reflects cash flow, seasonality, and repayment behaviour. This profile becomes the basis for a data‑backed loan offer, with the data set itself acting as collateral.

Dozie explains that the model works in three steps:

1. Data capture: Every transaction—whether a grocery sale on a POS or a ride‑hailing fare—feeds into Sparkle’s secure data lake.
2. Risk modelling: AI engines analyse patterns, flag anomalies, and assign a risk score that updates daily.
3. Loan issuance: Based on the risk score, Sparkle extends a credit line that can be drawn down instantly, with repayment terms tied to cash‑flow cycles.

This approach reduces the need for physical security, shortens underwriting time from weeks to minutes, and, crucially, lowers the cost of capital for borrowers.

Impact on borrowing costs and financial inclusion

When risk is assessed through data rather than static assets, lenders can price loans more accurately. Sparkle’s pilot in Lagos and Abuja showed an average interest rate of 12 % per annum—significantly below the 18‑22 % typical for unsecured SME loans in 2025. Moreover, the approval rate jumped from 28 % to 63 % among businesses that previously could not meet collateral requirements.

Dozie notes that the model also benefits banks. By reducing default risk through continuous monitoring, banks can free up capital, improve their loan‑to‑deposit ratios, and comply more easily with the Central Bank’s risk‑weighting guidelines introduced in 2024.

Regulatory backdrop and the role of the CBN

The Central Bank of Nigeria (CBN) has been encouraging fintech innovation since the launch of the Digital Financial Services (DFS) framework in 2023. In 2025, the CBN issued a circular allowing “alternative data” to be used for credit scoring, provided that data privacy standards are met. Dozie welcomes the move, saying it validates Sparkle’s strategy and opens the door for wider industry adoption.

However, he cautions that regulators must balance innovation with consumer protection. “We need clear guidelines on data ownership, consent, and the right to contest a credit decision,” Dozie stresses. Sparkle has already partnered with the Nigeria Data Protection Regulation (NDPR) office to embed consent‑driven data sharing into its onboarding flow.

Scaling the model across Africa

While Sparkle’s focus is Nigeria, Dozie sees a continent‑wide opportunity. Ghana’s mobile money penetration, South Africa’s sophisticated fintech ecosystem, and Kenya’s M‑Pesa legacy all provide rich data sources that can be transformed into credit collateral. He envisions a pan‑African data‑exchange platform where verified transaction histories travel across borders, enabling SMEs to access credit in multiple markets without duplicating paperwork.

To that end, Sparkle is in talks with fintech hubs in Nairobi, Accra, and Johannesburg to pilot cross‑border data sharing. If successful, the model could help address the estimated $200 billion SME financing gap across Sub‑Saharan Africa.

Challenges and the road ahead

Adopting data‑as‑collateral is not without hurdles. Data quality varies widely; many informal traders still rely on cash transactions that leave no digital trace. Moreover, there is a lingering mistrust among some business owners who fear that sharing sales data could expose them to tax audits or competitive threats.

Dozie’s response is two‑fold: first, invest in low‑cost digital tools—such as QR‑code POS devices—that bring even the most informal sellers onto the data ecosystem; second, run education campaigns that demystify data usage and highlight the tangible benefits of lower‑cost credit.

Looking to 2027, Sparkle plans to launch a “Data‑Collateral Credit Score” badge that SMEs can display on their websites and social media, signalling to suppliers and investors that they have a verified, AI‑backed credit profile.

FAQ

• What is meant by “data as collateral”? It refers to using a borrower’s digital transaction history—sales, payments, and cash‑flow patterns—as a security instrument for a loan, instead of physical assets.
• How does Sparkle protect my business data? All data is encrypted at rest and in transit, stored on secure cloud servers, and accessed only with explicit consent. Sparkle complies with the NDPR and CBN’s alternative‑data guidelines.
• Will using data lower my loan interest rate? By providing a real‑time view of cash flow, data‑driven risk models can price loans more accurately, often resulting in lower rates than traditional unsecured loans.

Uzoma Dozie’s vision of turning everyday business transactions into a trusted credit asset could reshape Nigeria’s SME financing landscape. As banks, regulators, and entrepreneurs align around data‑centric lending, the long‑standing collateral blind spot may finally be bridged, unlocking growth for millions of small businesses across the continent.

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