Optasia Plans to Use Telco Infrastructure for Banks

Optasia has spent years teaching telcos like MTN and Vodacom how to lend money to their own subscribers. The thesis was straightforward: reliable airtime bill payments over 18 months predict creditworthiness better than a thin credit file showing three late payments from five years ago.

Telcos adopted the model, the loans performed and Optasia processed billions in transactions.

Now Optasia is presenting the same approach to banks. The proposition rests on a tension: banks have long positioned credit bureau scores as the primary reliable method for assessing risk. Telcos, operating outside traditional financial services, built profitable lending books using different data.

What Optasia Provides
The company operates as infrastructure rather than lender. It provides credit decisioning, loan management, and collections systems that enable telcos to offer airtime advances, handset financing, and small loans. The model resembles banking-as-a-service built for organisations with customer relationships and transaction data but no banking licences.

The MTN and Vodacom programmes function because telcos observe granular payment behaviour. A customer who tops up R50 every Monday morning for two years without missing a week demonstrates financial discipline. Traditional credit bureaus cannot capture this information because prepaid airtime purchases are not reported to credit reference agencies.

The Banking Opportunity

South Africa has approximately 24 million credit-active consumers. An additional 15 to 20 million adults are financially active but credit-invisible. They earn income, pay rent, purchase electricity, and top up airtime, but maintain thin or non-existent credit bureau files.

Banks typically reject these applicants. The rejection stems from data limitations rather than demonstrated credit risk. Traditional scoring models cannot assess them.

Optasia's position is that banks possess transaction data that could address this gap. Transaction history including salary deposits, utility payments, and retail purchases can inform scorecards predicting repayment behaviour. Telcos demonstrated this approach at scale.

Implementation Challenges

Banks have built regulatory infrastructure around credit bureau data over decades. Lending outside these established frameworks requires explaining to regulators why loans that score as subprime on traditional models receive approval when alternative data suggests lower risk.

Business model considerations also exist. Banks currently generate substantial margins from thin-file customers precisely because these segments are underserved. Expanding access through alternative data could increase volume while compressing margins. A bank serving 100,000 customers at 25% rates faces different economics than serving 300,000 customers at 18% rates, even when aggregate profitability potentially increases.

Risk assessment presents another consideration. Credit bureaus represent socialised risk across the industry. When a loan defaults after receiving a positive bureau score, the decision faces limited scrutiny. When a loan defaults following approval based on airtime payment patterns or utility bill history, the bank must defend the methodology.

Market Context

The telco lending success demonstrates that credit scoring approaches represent strategic choices rather than technical constraints. Banks could have developed alternative data models earlier. The existing system served their target customer base adequately.

Telcos innovated partly because they maintained subscriber relationships without credit infrastructure. They were positioned to explore alternative approaches.

Banks now face questions about whether customer exclusion resulted from assessment inability or market strategy.

Potential Scenarios

Gradual bank adoption 
One or two banks may pilot alternative data programmes targeting specific segments such as gig economy workers, recent graduates, or informal traders. These initiatives would likely be framed as financial inclusion efforts. Optasia would secure limited contracts. Thin-file customers would gain access, possibly at premium pricing initially.

Fintech competition
Without bank movement, fintech lenders using alternative data could build lending portfolios from bank-rejected customers. After proving the model's viability, these fintechs would scale independently or sell to banks. Banks would pay acquisition premiums for capabilities they could have developed internally.

Regulatory intervention 
The Reserve Bank has emphasised financial inclusion objectives. Regulatory requirements for banks to demonstrate use of all available creditworthiness data, not solely bureau scores, would position alternative data as compliance rather than innovation.

Market Dynamics
Optasia's value proposition to banks contains an implicit comparison. Telcos without traditional financial services expertise have lent profitably to thin-file customers using behavioural data.

Banks maintained profitable operations serving credit-visible customers. Whether competitive pressure, regulatory requirements, or margin opportunities in unbanked markets drive adoption remains uncertain.

The telco programmes establish proof of concept. Bank adoption depends on how institutions weight regulatory considerations, business model implications, and competitive positioning against demonstrated alternative data performance.