The future of credit data: Will AI reduce bureau dependency?

Lenders have traditionally turned to credit bureau data to assess affordability and risk—but that reliance is starting to change. AI-powered models are now delivering real-time, predictive insights that offer far more than static bureau scores. And with alternative data sources gaining traction, credit providers are under pressure to reassess how they evaluate risk.

Here’s what we’re seeing:

• AI models are tapping into real-time transactional data and behavioural signals to build a more dynamic view of risk.

• More lenders are questioning whether costly bureau subscriptions still deliver strong ROI.

• We could see bureau pricing models evolve as the appetite for high-frequency, high-value data increases.

Are credit bureaus becoming obsolete? Not at all. But lenders who stick with the status quo risk overpaying for outdated insights—or missing the opportunity to switch to more effective, better-value alternatives.

Let’s take a closer look.

Will AI replace traditional bureau data or complement it?

For decades, credit bureau data has been core to lending decisions. A borrower’s credit score, past repayments, and financial history have dictated everything from mortgage approvals to credit card limits. But AI is starting to rewrite the rules.

AI’s big advantage: Real-time, predictive risk insights

Unlike traditional credit bureau reports, which rely on historical data, AI-driven models can:

✅ Analyse real-time transactional data (e.g., income streams, spending patterns, account balances).
✅ Identify early warning signs of financial distress before missed payments occur.
✅ Detect thin-file and no-file borrowers who might otherwise be overlooked by traditional scoring.

Instead of looking at whether someone paid their credit card on time last year, AI models assess if they can afford new credit today.

Where traditional credit data has limits
Credit Reference Agencies (CRAs) offer a useful view of someone’s financial past—but there are clear limitations:

• Static picture: Credit reports don’t capture real-time affordability. A person might have a strong credit score but still be struggling to meet current financial commitments.

• Missing segments: Bureau data often overlooks certain groups, including younger borrowers, the self-employed, and those with limited or non-traditional credit histories.

• Lagging indicators: Conventional scoring models can be slow to reflect rapid changes in the economy—such as rising living costs affecting people’s ability to repay.

Why credit bureaux still matter (for now)

AI may be advancing fast, but credit bureaux continue to play an important role. In many regulated sectors, bureau scores are still a core requirement. They also hold extensive historical data—something AI models can use to enhance predictive accuracy.

This won’t replace the bureaux. But it's likely to see more credit providers switch towards hybrid models. The most forward-thinking lenders are blending bureau data with AI-driven insights to create more accurate, adaptable risk frameworks.

So, how should this influence lenders’ approach to bureau pricing and procurement? Let’s take a look.

What this means for credit bureau pricing & procurement

If AI-driven models can provide faster, more adaptive risk insights, lenders may start relying less on traditional credit bureau data—or at least, use it differently. This development could have big implications for credit bureau pricing and procurement strategies.

1. Lenders may need less bureau data (or different types)

• AI is reducing the need for full bureau reports in favour of more targeted, high-value data sets.

• Lenders focused on real-time affordability assessments may prioritise alternative data sources (e.g., Open Banking, transactional data) over traditional static credit scores.

• Some businesses are already cutting back on bulk data subscriptions—instead, they’re paying for only the most relevant insights.

2. Credit bureaux will adapt their pricing models

• If demand changes, CRAs may move toward pay-for-use models instead of fixed annual contracts.

• More competition from AI-powered alternative data providers could force bureaux to reprice their offerings.

• Lenders who regularly benchmark their CRA costs will have stronger leverage to negotiate better deals.

3. What this means for procurement teams

• With AI reducing reliance on bureaux, lenders should reassess their data contracts to avoid overpaying.

• Procurement teams should demand more pricing transparency—as AI challenges the status quo, long-term CRA contracts may not make sense.

• Benchmarking bureau pricing will become even more critical to ensure lenders are getting market-aligned pricing.

With AI changing the way risk is assessed, credit providers must rethink their data strategies now. Here’s what that should look like…

How lenders should rethink their data strategy now

AI isn’t replacing credit bureaux overnight, but it is changing the value and role of traditional credit data. Here’s how lenders should start rethinking their approach:

💡Move toward a multi-source data strategy

• The best risk models will blend AI insights with bureau data for a more complete borrower profile.

• Open Banking, transactional data, and behavioural insights will become essential alongside traditional CRA reports.

• Rather than relying on a single bureau, lenders should consider multi-bureau or hybrid data approaches for a more resilient risk assessment framework.

💡Benchmark bureau costs regularly

• As AI changes how credit data is used, some lenders may be overpaying for data they don’t need.

• Many lenders renew CRA contracts without realising they could negotiate better terms—especially as alternative data sources become more viable.

💡Push for more flexible procurement terms

• Long-term bureau contracts could become less relevant as lenders rely on a mix of alternative and bureau data.

• Lenders should negotiate more adaptable pricing models, such as pay-for-use structures instead of bulk subscriptions.

• Key question for procurement teams: Are you paying for the right data, at the right price, with the flexibility to adapt as AI-driven insights evolve?

Final thoughts

AI isn’t making credit bureaux obsolete—but it is changing how lenders buy and use data. The smartest lenders will:

✅ Blend AI insights with bureau data to improve risk assessment.
✅ Benchmark their bureau costs to avoid paying for redundant data.
✅ Negotiate flexible contracts that reflect the evolving role of AI in credit decisioning.