AI is giving financial services a far richer picture of a business’ background and future trajectory giving more insights and flexibility to enable the automation of credit assessments. But it’s important that the distinctions between what’s possible to automate and what should be automated are clearly set out.
Historically, lending decisions have relied heavily on relatively static information: credit history, income, assets and financial performance. Today, open banking and emerging finance models provide a much more dynamic view of businesses, including cash-flow and transaction data.
This matters because AI can process vast amounts of information, structured or unstructured, in seconds, identifying patterns and potential risks that would take considerably longer to assess manually, whilst also highlighting new growth opportunities that would otherwise go unnoticed.
It's these advancements and proof points - as well as our consistent drive to improve customer experience - that led us at Simply Asset Finance to adapt this with our AI business assistant, Kara.
Seizing the opportunity
By automating time-consuming operational tasks - from processing proposals and extracting information to routing requests and carrying out due-diligence checks - Kara helps lending teams work faster and more efficiently, freeing them up to work with customers to ask key questions, make informed decisions and guide them through the process.
That speed matters, particularly for SMEs, where funding requirements are often time-sensitive and delays can mean missing a ‘make or break’ opportunity, holding up a transaction or slowing growth. By reducing unnecessary friction in the process, technological solutions like Kara can help businesses access the finance they need when it matters most.
With this in mind, it’s important to recognise that the objective of AI involvement isn’t to replace people, but to remove additional work around lending so more time can be focused on the things that require experience, judgement and relationships. More information about a business isn’t necessarily the same as understanding the business, its individual history and the people working for it on a daily basis.
Context is key
Whilst AI is exceptionally good at processing information consistently, identifying patterns, flagging potential risks and interpreting complex signals, the context behind those signals can be much harder to capture.
A business might show a temporary drop in cash flow because its owner has invested heavily in new equipment. Another might be seeking finance precisely because it has identified an opportunity to expand into a new market. This is where face-to-face relationships and regular meetings remain a crucial part of understanding the motive behind the data findings. Partnerships aren't built through simply understanding the numbers, they develop through mutual respect and demonstrating trust and investment in the cause - qualities that raw metrics and rapid speed simply cannot replicate.
Understanding a company's ambition requires looking far beyond its current numbers, whether that means investing ahead of growth, entering a new market, or pursuing an untested opportunity. AI can analyse historical data, but it can’t sit down across a table, look a business owner in the eye, and truly understand where they want to be in five or ten years.
Deciphering that vision demands human connection and standard-setting conversation to grasp the strategic reasoning behind a decision that financial statements cannot yet capture. Ultimately, the difference between pure data analysis and genuine insight lies in the strength of these human relationships.
Technology with a handshake
Our approach of ‘Technology with a Handshake’ suggests how tools like Kara help teams to work efficiently whilst ensuring that there’s sufficient understanding of the story behind the application - particularly when a business is taking a calculated step towards growth.
The answer isn’t to choose between AI and people, but to understand where each is most valuable, and how they can complement each other, as well as make assessing ambition stronger.
Growth rarely follows a perfectly predictable path, and businesses respond to new opportunities, changing markets and unexpected challenges, which won’t always be captured by financial data alone. The opportunity for the lending industry is therefore not simply to automate more, but to put more emphasis on the use of technology while protecting the human judgement that makes good lending possible.