Open Banking AI: Building Intelligent Financial Products on PSD2 and Open Banking APIs

Open banking gives AI the data it needs

AI in financial services has always been constrained by data access. Banks have rich transaction data but keep it siloed. Fintechs have innovative AI capabilities but limited data to feed them. Open banking changes this equation.

In the UK, the Open Banking Implementation Entity (OBIE) mandates that the nine largest banks share customer data (with customer consent) through standardised APIs. In the EU, PSD2 establishes similar requirements across member states. The result: with customer permission, any authorised financial company can access transaction history, account balances, and payment initiation across multiple banks.

This is enormously powerful for AI applications, because the AI can now see a customer’s complete financial picture — not just what happens within one institution. We’ve built financial products that leverage this data, and the applications are more varied than most people realise.

What you can build with open banking + AI

Account aggregation with intelligence is the foundational application. Show customers all their accounts in one place — that’s table stakes. The AI layer adds value by categorising transactions automatically (rent, groceries, subscriptions, transfers), identifying recurring payments, detecting unusual spending patterns, and providing a unified view of the customer’s financial health across all their accounts.

Spending intelligence goes deeper. AI analyses transaction patterns to answer questions customers actually care about: “Am I spending more this month than usual?” “Which subscriptions am I paying for that I’m not using?” “How much can I safely save this month given my upcoming bills?” These features require ML models trained on spending categorisation, cash flow prediction, and anomaly detection.

Alternative credit scoring is one of the most impactful applications. Traditional credit scoring relies on credit bureau data, which excludes millions of thin-file consumers — people who’ve never had credit, recent immigrants, younger adults. Open banking data provides an alternative signal: regular rent payments, consistent income deposits, responsible spending patterns can all indicate creditworthiness. SuitsMe’s customer base — migrants and seasonal workers, many without traditional credit histories — is exactly the population that benefits most from this approach.

Personalised financial product recommendations use the complete financial picture to suggest relevant products: a savings account optimised for the customer’s cash flow pattern, a credit card that matches their spending categories, an insurance product for the specific risks their financial profile reveals. This is where the AI creates direct commercial value — not by guessing what the customer might want, but by knowing their financial situation well enough to make genuinely useful recommendations.

Cash flow forecasting for SMEs is an emerging application. By analysing business transaction data from open banking feeds, AI can predict cash flow weeks or months ahead — when invoices are likely to be paid, when recurring expenses will hit, and whether the business is trending toward a cash gap. This is valuable for both the SME (better financial planning) and for lenders (more accurate assessment of lending risk).

Technical architecture

An open banking AI system has three layers. The data layer connects to open banking APIs (either directly or through aggregators like TrueLayer, Yapily, or Plaid), handles authentication (OAuth 2.0 with strong customer authentication as required by PSD2/SCA), and normalises data from different banks into a consistent format. Normalisation is more work than you’d expect — banks categorise transactions differently, use different reference formats, and sometimes provide data at different levels of granularity.

The intelligence layer runs ML models on the normalised data: transaction categorisation (typically a fine-tuned classifier running on merchant names and transaction descriptions), cash flow prediction (time series models), anomaly detection (statistical methods or autoencoders), and credit scoring models where applicable.

The application layer presents insights to users or integrates them into downstream products. This is where LLMs add value — translating model outputs into human-readable insights. “Your spending on dining out increased 34% this month compared to your 3-month average” is more actionable than a raw number.

Regulatory considerations

Open banking AI operates in a particularly regulated environment. You need authorisation as an Account Information Service Provider (AISP) to access data, or a Payment Initiation Service Provider (PISP) to initiate payments. In the UK, this means FCA authorisation. In the EU, it’s authorisation under PSD2 from the relevant national authority.

Strong Customer Authentication (SCA) requirements mean you can’t just store credentials — you need to re-authenticate regularly, which creates UX challenges that your application needs to handle gracefully.

GDPR/UK GDPR applies to all personal financial data you process. Purpose limitation (you can only use data for the stated purpose), data minimisation (don’t process more than you need), and storage limitation (don’t keep data longer than necessary) all apply. For AI applications that might use transaction data for training models, this requires careful consideration of data processing agreements and consent mechanisms.

“Open banking is one of the few regulatory initiatives that genuinely creates new market opportunities for technology companies. The data is there, the APIs are standardised, the customer consent framework exists. The remaining challenge is building AI applications that use this data to create real value for customers — not just dashboard features that look impressive but don’t help anyone.”

Getting started

For most fintechs building on open banking, start with an aggregator (TrueLayer, Yapily) rather than connecting to bank APIs directly. The aggregators handle the complexity of different bank implementations, SCA flows, and data format variations. Budget $5K–$15K/month for aggregator fees at moderate scale.

The AI layer depends on your application. Transaction categorisation: $15K–$30K to build and train, 4–6 weeks. Cash flow forecasting: $25K–$50K, 6–8 weeks. Alternative credit scoring: $40K–$80K, 8–12 weeks (more complex due to model validation requirements and potential FCA scrutiny). Full financial management platform with all of the above: $100K–$250K, 4–8 months.

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Building on open banking data? Contact us — we’ll help you design the AI layer that turns transaction data into genuinely useful financial products.