Commercial finance brokers, also known as business loan brokers or business finance brokers, face numerous challenges in 2025 and beyond.
As Deloitte reports, the brokerage business model is relatively stable, yet brokers must contend with changes, including:
The list goes on…
In sum, it can be challenging to be a commercial finance broker in the 2020s. Technology, and, in particular, AI, can offer a helpful solution for common broker bottlenecks. AI’s ability to continuously learn can make it an attractive alternative to traditional broker tech. Let’s dive in.
Much of the language surrounding AI for brokers focuses on vague promises. ‘Reducing ongoing operational costs’, ‘data-driven decision-making’ and ‘providing insights’ are cliches you might expect to see on broker tech websites. The problem with phrasing like this is that it’s not illustrative of how the AI has been deployed and its specific purpose.
Two practical approaches to deploying AI in brokerage contexts exist – automating mundane tasks and introducing AI-powered systems.
Let’s say that your firm’s operational model relies on a manual, repetitive process. Examples of these automatable processes may include:
Examples of documents brokers might wish to extract from include bank statements, identity documents, etc. Manual data capture is usually tedious and prone to errors.
Brokers may end up doing simple data fixes, such as standardising dates, currencies, etc. Moreover, if you’re already using an automated data extraction solution, you might end up tweaking the post-processed data, which can add time to your busy schedule.
If you find your inbound website leads tend to ask the same questions (e.g. ‘What’s the lowest interest rate you can get me?’, ‘Can you guarantee me a loan?’ etc.), you can automate chatbot responses that offer generic advice or encourage them to schedule time with a broker (or both). Automating these client service processes can free up time and prevent frustration.
AI can also be used to automate fraud detection processes. Many manual fraud prevention processes - such as verifying clients' documents, researching clients' financial history and source of funds and record-keeping - can now be automated via AI.
We asked the brokers at Clifton Private Finance about how they integrate AI, and they flagged the benefits of automated facial recognition and document automation.
“It’s no secret that AI is revolutionising commercial finance, as it is for many business models right now. In the commercial finance space, it’s largely being used for automating repetitive tasks like document analysis (trawling through huge volumes of business accounts and financial forecasts, for example).
But it can also be used to enhance fraud detection – at Clifton Private Finance, we’ve also embraced tools like facial recognition to streamline processes and carry out extra checks beyond our existing frameworks.
Ultimately for our clients, it means our brokers can free up time to focus more on personalised advice instead of the administrative sides of our work. However, the human touch remains critical—it’s the balance of tech and empathy that builds lasting client relationships and leads to the best client outcomes.”
Clifton's use case highlights the importance of funnelling the saved time into client-facing activities. Automation shouldn't mean isolation - despite the augmented role of technology, clients should actually spend more time with real, experienced human brokers.
You might choose to integrate with a new AI platform specialised for brokers. These platforms are often tailored towards customer relationship management (CRM) platforms, online lending marketplaces and credit scoring platforms. Rather than automating back-office processes like extracting data, these platforms efficiently connect brokers with potential customers.
We highly recommend investing in a Proof of Concept (PoC) for all-encompassing, end-to-end solutions like these. Checking the solution’s functionalities and culture fit will ensure it’s worth investing in.
UKGI recently released an article boldly titled, ‘Why Brokers Could Never Be Displaced by AI’. Its central thesis is that customers prefer talking to human brokers to automated AI systems. For example, human brokers can pick up on subtle cues that could allow greater support to vulnerable customers. Human communication bolsters brokerage success, which AI can never convincingly replicate.
The fact that clients prefer talking to humans is indisputable. In a Gartner survey earlier this year, 64% of customers claimed they would prefer companies not to use AI for customer service. Over time, as using AI becomes more commonplace, people may become more accepting of interacting with online AI-powered systems (such as client portals, brokerage apps, online payment gateways, etc.). However, most clients will prefer the option of switching from talking to an AI agent to a human.
Managing colleague attitudes towards implementation is more difficult. AI’s inclusion in commercial finance brokerage is inevitable at this stage, but it is up to the company to handle the transition into using AI technologies. The tone of UKGI’s article seemed to fall into mistrust in certain places, which is exactly how you can anticipate some colleagues could react to implementing AI. Companies must carefully manage these fears of becoming obsolete, with emphasis given to the parameters of AI’s role in the organisation.
Though the future role of AI in commercial finance brokerage isn’t set in stone, here are two predictions.
Many broker tech solutions promise to give a competitive edge to the user. But if AI integration is inevitable for all brokers, how can your firm maintain a competitive edge?
The answer is to ‘mix and match’ AI technologies, experimenting until you find one that matches your firm’s Unique Selling Point (USP).
Therefore, you might consider trying out more than one solution. Matt Hicks, the chief commercial officer of insurtech firm, Recorded, told the Insurance Times that brokers should consider multiple tech providers to meet their needs.
A simple example of this in action is if you promise a speedy and efficient brokerage service. Your website might use an AI-powered chatbot to push inbound leads further down the pipeline. You might also use AI-based data extraction technology to extract data from uploaded financial documents quickly. The result would yield faster onboarding and communication, hopefully driving up client satisfaction rates.
One barrier to effective client relationships might be an overreliance on AI technology. AI-powered chatbots, though helpful for initial interactions, may diminish opportunities for building strong, personal relationships with clients. If broker techs and brokers focus too much on automating client communication – a skill intrinsic to all brokers – it may prevent them from building long-lasting relationships with clients.
AI’s agility may assist today’s brokers in building better relationships (while making their lives easier). However, successfully implementing AI requires transparency and a goal-orientated approach. By diagnosing inefficiencies in your current operational model and understanding what’s available on the broker tech market, you can implement technologies that give your firm a competitive advantage - no cliché necessary.
Interested in automating data extraction from essential brokerage documents? Contact our financial data quality team to learn more - book a demo or email hello@evolution.ai.
