The £7bn question
ClearScore wants you to know that 17 million Britons are being denied credit they could safely handle, and that outdated assessment models are costing the UK economy £7bn annually. The solution, according to the credit-checking firm's newly released white paper, lies in open banking data and artificial intelligence—technologies that would allow lenders to make what it calls "precision" decisions about who deserves a loan.
The pitch is seductive. Why rely on crude credit scores when you could analyse months of bank transactions, spot patterns in spending behaviour, and identify responsible borrowers who traditional models miss? What's interesting here is the timing: as inflation squeezes household budgets and consumer debt climbs, a firm whose business model depends on connecting people with credit products is arguing that the real problem is we're not lending enough.
ClearScore's figures demand scrutiny. The £7bn GDP boost is a projection, not an established economic fact, and the methodology behind it remains unclear from the white paper itself. Similarly, the claim that 17 million adults have "unmet credit needs" representing a £2bn supply gap raises an obvious question: unmet according to whom?
The company's own data suggests 60% of consumers currently receiving no credit offers may actually be creditworthy when assessed using open banking information. Perhaps they are. Or perhaps there are reasons—debt-to-income ratios, irregular earnings, existing obligations—that traditional lenders have learned to treat as red flags.
Open banking has been available in the UK since 2018, allowing third parties to access bank transaction data with customer consent. Adoption for credit decisioning has grown slowly, not because lenders are Luddites, but because financial regulators remain deliberately cautious about expanding credit access. The 2008 financial crisis wasn't caused by too little lending. It was caused by too much lending to people who couldn't afford to repay.
When precision becomes predation
Tom Markham, ClearScore's chief commercial officer, insists this isn't about lowering standards but "raising precision." The distinction matters less than he suggests. Precision in identifying who will repay is valuable. Precision in identifying who can technically make minimum payments whilst falling into a debt spiral is something else entirely.
The Financial Conduct Authority introduced its consumer duty rules in 2023, explicitly requiring firms to ensure products are affordable and provide fair value. This wasn't regulatory whimsy. It followed years of concern about buy-now-pay-later schemes, high-cost credit, and products marketed to vulnerable consumers who could service debt but never escape it.
ClearScore notes that where open banking is already used for credit decisions, marketplace lending volumes have increased by 14%. This tells us that more credit is being extended. Whether that credit is sustainable, whether borrowers are better off, and whether defaults remain low over a full economic cycle—that data doesn't appear in the white paper.
The business case behind the public interest
Credit marketplaces like ClearScore generate revenue by connecting consumers with lenders. More credit extended means more commission earned. This doesn't make their arguments wrong, but it does mean their economic interest aligns perfectly with their policy recommendations.
The firm positions itself as championing financial inclusion, arguing that "responsible credit is not a social cost—it's an economic enabler." There's truth in that. Access to affordable credit can help people manage emergencies, invest in education, or start businesses. Equally true is that irresponsible credit destroys household finances and creates systemic risk.
AI and open banking data can certainly identify patterns that traditional credit scores miss. Someone with a thin credit file but stable income and consistent saving habits probably is a better risk than their credit score suggests. The technology works. The question is what happens when competitive pressure and profit incentives push lenders to define "stable enough" downward.
What happens next
Financial services lobbying typically follows a familiar pattern. Industry identifies regulatory constraint. Industry commissions research showing enormous economic benefits to relaxation. Industry frames the debate as innovation versus stagnation.
ClearScore's white paper follows this template precisely, right down to the call for "regulatory certainty" that would give lenders confidence to expand. Translated from corporate communications language, this means clearer signals about how far regulators will permit credit standards to stretch.
The FCA faces a genuine dilemma. Credit markets are inefficient, and some creditworthy borrowers are indeed locked out by outdated assessment methods. Open banking data offers real advantages. Yet the regulator's core responsibility is consumer protection, not GDP optimisation.
Whether AI-driven lending expands will ultimately depend on evidence, not projections. If early adopters can demonstrate lower default rates and better consumer outcomes across economic cycles, the case strengthens. If defaults rise as competition intensifies and standards drift, we'll have learned an expensive lesson about the difference between precision and prudence.
ClearScore has painted an optimistic picture of technology-enabled growth. Regulators, scarred by previous crises, will want to see the data. The rest of us might reasonably wonder whether algorithms that "unlock" billions in new lending are genuinely better at assessing risk—or simply better at justifying it.
