Has all this ‘AI adoption’ by big banks really made getting business loans easier yet?

Banks and financial companies talk a lot about using artificial intelligence. Most banks around the world are using AI in some way and many are moving from testing to real use in daily operations. Some major institutions are working directly with AI providers to push these efforts forward. For example, BBVA is expanding a partnership with OpenAI to roll out advanced AI tools to hundreds of thousands of employees to improve customer experiences and internal work.

Some surveys show that over 75 percent of banks have launched some type of generative AI application, and adoption continues to rise.

If AI is now widely used by banks, why do many business owners still find lending slow and hard to navigate?

To answer that, it helps to look at each stage of the business loan process and see where AI is having impact and where it has not yet made a major difference.

1. Finding the right lender

The first step for many businesses is figuring out which lender might be a good fit. Even though AI tools are widely used in banking, most banks do not offer smart match systems that automatically show a business where it has the best chance of approval based on real data. Many lenders still expect applicants to search and compare on their own.

At this stage, AI often supports internal segmentation and marketing rather than helping a business find the right loan.

2. Preparing application data

Once a business finds a lender, the next step is to gather documents and financial data. Businesses must usually upload bank statements, tax returns, accounting records and other files. This work feels repetitive and time consuming.

AI tools are good at extracting data from documents and cleaning it up, but most banks have not yet fully adopted this type of automation for customer submissions. AI adoption in credit is still stronger in areas like fraud detection and internal analysis than in transforming the way loan data is collected.

3. Underwriting and credit decisions

AI does play a growing role in underwriting. Tools can help with risk signals, credit scoring enhancements and spotting patterns in large data sets. Many banks use AI in these areas now.

However, most major lenders still rely on human review for final decisions, especially for business loans where context matters. Models can support an underwriter by highlighting risk factors or analysing trends, but they do not usually replace human judgment.

4. Approval and funding

After a decision is made, a business still faces steps like compliance checks, legal documentation and the final transfer of funds. Even with AI support, these operational steps often involve human teams. AI can speed parts of the process but does not yet create frictionless, automatic funding in most cases.

How AI is actually helping today

So what is AI currently doing in financial services if it is not making the entire loan process easy?

AI is widely used for:

• detecting fraud in transactions

• improving internal operations

• allowing smarter analytics on customer and risk data

• automating routine questions and customer support

• reducing manual work in compliance and risk monitoring

These uses improve bank efficiency and often help customers indirectly. But the everyday experience of getting a business loan has not yet changed dramatically for many companies.

Where real innovation for SME lending could come from

One of the biggest obstacles in lending today is data fragmentation. Small and medium businesses have financial information spread across accounts, platforms and public records. AI can help make that data cleaner and more standard before an application reaches a lender.

Some newer lending platforms are already using AI to:

• combine public and private data into consistent formats for use of auto-fills

• route applications only to lenders with matching criteria

• reduce repeated submissions and improve match quality by creating lending profiles witth company details

These uses of AI can cut down the time it takes from application to decision and make the process feel less manual for borrowers. Funding Agent are using enrichment and routing logic to structure SME applications before they reach lenders, improving match quality, reducing unnecessary decline cycles. Broker companies would benefit most from ai usage in this part workflow.

Conclusion

AI adoption by banks is now widespread and growing fast. Many lenders are moving from pilots to real production use of AI to improve efficiency, customer service and risk analysis.

However, for many business owners the loan process still has manual steps and friction. That is because the biggest barriers are not only about decision models. They are about the flow of data, the matching of businesses to lender rules, and the structure of credit processes themselves.

AI today is helping banks do more work faster, but it is not yet delivering fully automated, smooth loan journeys for every business. The real opportunity for transformation is in cleaning and organising data before it gets to a lender, improving match quality, and reducing wasted time on repeated applications.

If lenders and platforms focus more on these practical points, AI could make business lending truly easier in the future.