Open banking and AI can speed up SME funding. They cannot fix weak cash flow.
Open banking and AI can make SME funding faster, clearer and less manual. They help a lender see cash trends, affordability, payment behaviour and warning signs much earlier.
But they cannot fix weak cash flow. Better data does not turn poor margins, unreliable customers, bad records or a loss-making business into a safe funding risk.
Quick summary
- Open banking lets a business share bank data securely with a funder or adviser.
- AI can review bank transactions, accounting data, cash trends, invoice patterns and risk signals faster.
- This reduces manual work and speeds up funding decisions.
- Technology does not remove the need for proper credit judgement.
- A business still needs a real funding purpose, clean records and a credible repayment route.
- The best use of AI is not replacing people. It is helping experienced people see the right questions sooner.
The business problem
Most SMEs do not wake up thinking about financial technology. They think about practical pressure: paying wages, buying stock, funding a new order, replacing a vehicle, covering VAT, managing late payment, keeping suppliers onside, and taking an opportunity without draining the bank account.
When they apply for funding, they want a fast answer. The lender wants evidence. That is where the old process often feels painful: PDFs, spreadsheets, bank statements, management accounts, debtor reports, emails, missing documents and repeated questions.
Open banking and AI can improve that process. They help collect, organise and interpret information faster. But faster is only useful if the answer is still sound.
Why this is topical
Open banking is now a normal part of the UK finance landscape. Open Banking Limited reported 16.5 million UK user connections by December 2025, while the British Business Bank's 2026 market report highlighted increased use of flexible finance to support smaller business cash flows.
AI is moving quickly across financial services too. The Bank of England and FCA's 2024 survey reported that 75% of responding firms were already using AI, with a further 10% planning to within three years.
The direction is obvious. Funding decisions will increasingly be supported by better data, faster analysis and smarter workflow tools. The important word is supported.
How open banking can help funding
Open banking lets a business share bank transaction data with a lender or platform, usually with consent and for a specific purpose. For a funder this can be more useful than a static set of accounts, because accounts are historic while bank data shows what is happening now. It can reveal:
- Cash coming in and going out
- Customer receipts and supplier payments
- Wage and tax payment patterns
- Loan repayments and returned payments
- Seasonality and cash headroom
- Whether the business is already under pressure
How AI can help funding
Used well, AI helps lenders and advisers process information more quickly. It can:
- Summarise bank statements and flag unusual cash movements
- Compare actual cash flow against forecasts
- Flag pressure around HMRC, wages or suppliers
- Spot inconsistent application information
- Review debtor concentration and payment patterns
- Draft questions for underwriters or relationship managers
- Help businesses prepare clearer funding packs and explain terms in plain English
That is valuable. It can make funding quicker, more consistent and easier to understand. But AI is not a magic answer machine. The commercial judgement still matters.
Where it works well
Open banking and AI work well when the business has a real funding need and the data supports the story. This is where funding is positive: better data helps a business show that the request is sensible, affordable and linked to a real commercial outcome.
Where it can go wrong
Technology can also create false confidence. A dashboard may look clean while margins are weak. An AI summary may read convincingly but miss context. A cash trend may look positive but be driven by unpaid tax, stretched suppliers or one-off receipts. The risk is confusing fast analysis with good credit. Common problems include:
- Poor quality accounting data
- Bank feeds that do not show the full group position
- Multiple accounts not being connected
- One-off transactions treated as normal trading
- AI summaries missing commercial context
- Over-reliance on scorecards
- Businesses misunderstanding what data they have shared
- Consent and privacy not explained clearly
- Fraud indicators ignored because the headline numbers look fine
The funding answer
The point of better data is not to make every application fit. It is to match the business to the right funding answer faster. A business waiting to be paid may need invoice finance. A business buying equipment may need asset finance. A business with moving cash needs may need a revolving facility. A business buying stock or fulfilling a large order may need trade finance, stock funding or another working capital structure.
Technology should help reach that answer quicker. It should also help say no quicker when the real issue is not timing but viability.
Costs, risks and watch-outs
Do not assume a tech-led funding process is automatically cheaper, safer or better. Watch for:
- Unclear pricing, or high fees hidden behind a fast approval
- Daily or weekly repayment structures that squeeze cash
- Poor explanation of what data is being accessed
- No human contact when something does not fit the model
- Limited ability to challenge a decision
- Facilities that can be reduced quickly if data deteriorates
- Personal guarantees added without proper explanation
- AI tools producing confident but wrong summaries
Fast funding is only good if the product fits and the cost is affordable.
Questions to ask before signing
- What data are you accessing, and for how long?
- Can I revoke access?
- How will open banking data affect the decision?
- Is AI being used to assess or summarise my application?
- Can a person review the decision if the system gets it wrong?
- What is the total cost, including all fees? Is there a minimum monthly fee?
- Can the facility be reduced if bank data changes?
- What security is required? Is a personal guarantee required?
- What happens if trading gets worse, and what could trigger default?
- Is this suitable for growth, survival, or both?
What lenders will check and why
Even with open banking and AI, lenders still check the basics. They may ask for bank statements, management accounts, filed accounts, aged debtors, aged creditors, invoice evidence, HMRC position, customer information, forecasts, existing lender details and Companies House records.
Due diligence is not box ticking. It is how the lender decides whether the request is real, affordable, evidenced and repayable. Most SMEs are honest, but a small number of bad actors manipulate applications, inflate values, hide liabilities or create false comfort. That makes funders more cautious and increases due diligence for everyone else. AI may help spot risk signals, but it does not remove the need for transparency.
Final practical summary
Open banking and AI should make SME funding better. They reduce friction, speed up questions, improve consistency and help businesses prepare stronger requests. But they do not change the fundamentals. Funding still needs a real purpose, a sensible product, a cost the business can afford and a clear repayment route.
Funding is a tool, not a failure. Better data should help good businesses use that tool with more confidence. It should not be used to dress up weak cash flow as something it is not.
Sources and further reading
- Open Banking Limited — Open Banking in 2025: now part of the UK's everyday financial life
- British Business Bank — Small Business Finance Markets Report 2026
- FCA — AI and the FCA: our approach
- Bank of England and FCA — Artificial intelligence in UK financial services 2024
This article reflects current Juno Funding editorial. Funding products, rates and lender appetite change frequently — figures are indicative only and should not be treated as advice.