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AI in business finance is moving from novelty to routine practice. The Department for Science, Innovation and Technology found that around one in six UK businesses currently uses at least one form of AI technology, but adoption is far higher in financial services: the Bank of England and FCA found that 75% of responding firms were already using AI and another 10% planned to do so within three years.
For business owners, that means AI is no longer sitting on the edge of the lending market. It is increasingly influencing how lenders process applications, how forecasts are built and how firms compare funding options.
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Optimisation and speed
The clearest change is speed. In the Bank of England and FCA survey, 41% of financial-services respondents were already using AI to optimise internal processes, 33% were using it for fraud detection, and 55% of all reported AI use cases involved some degree of automated decision-making.
That does not mean machines are replacing every credit decision. It means much more of the routine work around data gathering, anomaly detection, pattern spotting and file triage can happen faster and at a greater scale. For a business applying for finance, that can translate into less repetitive admin, quicker responses and a smoother path from first enquiry to lender review.
Decision support
The second change is better decision support. DSIT found that 75% of UK businesses already using AI said it had improved workforce productivity, 57% said it had improved processes or operations, and 34% said it had reduced production or operating costs. More than half of adopters also reported a rise in employee productivity after adoption, with 16% saying productivity had increased by 20% or more. In finance, that can mean stronger budgeting, more timely management reporting and earlier visibility of problems.
But the gains come with a warning: the Bank of England and FCA also found that 46% of firms had only a partial understanding of the AI technologies they used, while only 34% said they had a complete understanding, and major current risks included data privacy, data quality, data security, and data bias or representativeness.
Can I use AI to help get a business loan?
Yes, but it is best used as a preparation tool rather than a promise of approval. AI can help you prepare a cleaner case for borrowing by organising information, pressure-testing assumptions and helping you ask better questions before you apply. That matters because the government’s review of small business access to finance says businesses are sometimes rejected because they apply for the wrong type of finance or make errors in their application.
The same review says only 26% of UK SME employers reported seeking external advice or information in 2023, which suggests many firms are still approaching borrowing with limited guidance. Used properly, AI can help fill part of that preparation gap before you submit a business loan application.
Thinking like an underwriter
AI can also help you think more like an underwriter before you apply. You can test what slower sales, later customer payments or higher costs would do to affordability and whether the amount you want to borrow still looks sensible. That is useful in a market where demand for debt remains subdued.
The government says only 3.5% of UK SMEs apply for new or renewed external finance, and only 1.5% apply for bank loans. It also says that only 34% of SMEs planning to apply for new or renewed finance are confident their bank will agree to the facility, down from 56% in 2019.
Even so, AI cannot create lender appetite where the fundamentals are weak. If you are still refining the commercial case, it makes sense to work through how to validate a business idea before you borrow. And if you use AI to help draft projections or application notes, every number still needs checking: DSIT found that 84% of businesses using AI apply at least some human input or oversight to AI outputs, with 67% reporting significant checking.
Cash flow forecasting and financial planning
This is one of the areas where AI can add the most practical value. Small businesses do not usually struggle because they cannot read a profit and loss statement. They struggle because timing goes wrong. Research commissioned by the Department for Business and Trade and the Small Business Commissioner estimated that late payments cost the UK economy almost £11 billion a year, affect more than 1.5 million businesses and contribute to 14,000 business closures annually. Businesses affected by late payments were owed an average of £17,000 and spent an average of 86 hours a year chasing overdue money. Those are exactly the kinds of patterns that make cash flow forecasting and financial planning difficult when they are done manually or updated too slowly.
Working with data
AI helps because forecasting no longer has to rely on a spreadsheet updated once a month. With the right data, AI tools can detect seasonality, recurring payment behaviour and early warning signs more quickly, then update expectations as new transactions arrive. Academic research published on ScienceDirect notes that machine learning can outperform traditional statistical models in financial time-series forecasting.
Separate research in the International Journal of Forecasting found that improvements in predictive accuracy were closely linked to cost savings in cash management. The British Business Bank also notes that a cash flow forecast gives businesses a clearer view of their likely future position and helps them act before shortfalls become critical. In other words, AI does not remove the need for judgement, but it can make that judgement earlier, sharper and far more useful when deciding whether you need a buffer, a facility or no borrowing at all.
AI and access to funding
One of the strongest arguments for AI in business finance is that it can widen access to funding when traditional data tells only part of the story. The government says Open Banking allows third-party providers, with the customer’s consent, to use current-account data to improve credit risk assessment and help small businesses that might previously have struggled to obtain finance. The same review notes that challenger banks accounted for 60% of annual gross bank lending to SMEs in 2024.
Together, those shifts matter because they point towards a lending market that is more digital, more data-rich and less dependent on a single old-fashioned scorecard. For smaller firms with a patchy credit history but solid trading data, that can be a meaningful change.
World Bank report
The broader evidence points in the same direction. A 2025 World Bank analysis said alternative data can improve approval rates, reduce credit losses and enhance access to credit for underserved individuals and micro, small and medium-sized enterprises. The World Bank also warns that wider use of alternative data can create bias, discrimination and privacy risks, while the Bank of England and FCA identify data bias and representativeness as a major current risk in financial services.
Using AI to check business loans
For many borrowers, this is where the technology feels most immediately useful. You can use AI to check a business loan offer before you sign by asking it to summarise the total repayable amount, compare fee structures, explain a personal guarantee, test repayment scenarios and translate lender jargon into plain English.
That kind of first-pass review can save time in a market that still feels fragmented to many firms. The government says small businesses often do not shop around, tend to rely on their main bank and frequently leave too little time between applying for finance and their funding deadline. If AI helps you compare faster and prepare earlier, it can reduce the odds of making a rushed decision on a product that does not quite fit.
Make sure you consult with a professional before making any financial decisions. AI misinformation is rife and can often distribute incorrect information.
Implementing AI in your business loan application
But AI should be your first checker, not your final decision-maker. DSIT found that data security and output accuracy are among the most common challenges businesses face when deploying AI, and smaller businesses in particular often worry about whether outputs are correct. The Treasury Committee also heard evidence in 2026 that AI-driven credit decision-making can lack transparency and that AI search engines can mislead or misinform people when used for financial guidance.
So every quoted rate, fee, covenant and repayment assumption still needs to be checked against the lender’s actual documents. The smartest process is usually hybrid: use AI to organise information, compare options and highlight risks, then use a loans specialist like Rise Funding to interpret lender appetite, edge cases and presentation. That human layer still matters because business lending is rarely decided on raw numbers alone.
If you are looking for a loan, Rise Funding can help find the best option for you. Whether it’s a business loan or others, we’re here to help you make a decision with confidence.
Plus, applying with Rise Funding doesn’t affect business credit. Contact us via the form below, or get an instant business quote through our online questionnaire.
