New industry research points to credit decisioning, documentation, and funding as priority areas for technology improvement. For equipment finance providers, the practical AI opportunity starts with finding where work slows down today.<\/p>\n\n\n\n
AI has moved quickly from strategic discussion to operating reality in financial services. The question for equipment finance leaders is becoming more specific: where can it actually improve the economics, speed, and quality of a deal?<\/p>\n\n\n\n
The timing is important. The UK asset finance market continues to grow. The latest Finance & Leasing Association data shows new business increased 2% year over year in July 2026 and was 6% higher through the first seven months of the year. SME lending increased 8% in July, reaching a record \u00a325.6 billion over the preceding 12 months. Finance & Leasing Association<\/a><\/p>\n\n\n\n Volume, however, is only part of the story. As providers pursue growth, the operational demands behind each transaction become more consequential. Faster decisions, efficient documentation, access to reliable data and the ability to move a transaction through multiple workflows all influence how effectively a finance provider can convert demand into profitable business.<\/p>\n\n\n\n That is where the AI conversation gets interesting.<\/p>\n\n\n\n The Secured Finance Industry Technology Survey, sponsored by Solifi<\/a>, gives us a useful view into where equipment finance providers see the greatest need for improvement.<\/p>\n\n\n\n Among equipment finance respondents, 63% identified credit decisioning and scoring as the functional area most urgently needing technology advancement. Another 51% identified documentation and funding. And 71% said real-time or instant decision-making capability is important or critical to customers.<\/p>\n\n\n\n Taken together, those findings point towards an opportunity that is more practical than the broad conversation about AI might suggest.<\/p>\n\n\n\n Consider what happens during an equipment finance transaction. Information is collected from customers, brokers, and other parties. Credit teams assess the transaction and the customer. Documents are received, reviewed, and processed. Approvals are issued. Contracts are generated. Conditions have to be satisfied before funding. Information ultimately needs to flow into servicing and portfolio management.<\/p>\n\n\n\n Each transition creates an opportunity for friction.<\/p>\n\n\n\n Sometimes the problem is obvious, such as manually entering the same information into multiple systems. Others are less visible: a credit analyst searching for information across documents, an exception requiring someone to determine the appropriate next step, or an approved transaction waiting because information has not moved cleanly into the next workflow.<\/p>\n\n\n\n These are good places to look for AI opportunities because they begin with a business problem rather than a technology looking for one.<\/p>\n\n\n\n The 63% finding around credit decisioning deserves particular attention.<\/p>\n\n\n\n Speed has always mattered in equipment finance, but faster credit decisions cannot come at the expense of credit discipline. The more useful application of AI is therefore not simply “automated underwriting.” It is improving how effectively people can assemble, interpret, and act on the information required to make a sound decision.<\/p>\n\n\n\n AI can help organize information from multiple sources, identify relevant information within documents, surface exceptions, and support analysis. Combined with established business rules and workflows, these capabilities can reduce some of the administrative work surrounding a credit decision.<\/p>\n\n\n\n There is an important distinction here.<\/p>\n\n\n\n The objective should not necessarily be to remove people from consequential decisions. The objective is to give them better information, sooner, with less manual effort surrounding the decision itself.<\/p>\n\n\n\n The wider UK financial services market offers useful context. The joint Bank of England and Financial Conduct Authority research found that 75% of responding financial services firms were already using AI, with another 10% planning to use it within three years. Yet only 2% of reported AI use cases were fully autonomous. More than half involved some automated decision-making, while human oversight remained an important part of the operating model. Source: FCA<\/a><\/p>\n\n\n\n That is a more useful model for equipment finance than the assumption that AI inevitably leads towards fully autonomous lending.<\/p>\n\n\n\n The 51% of equipment finance respondents pointing towards documentation and funding also deserves attention.<\/p>\n\n\n\n Equipment finance remains document-intensive. Applications, financial statements, invoices, asset information, contracts, and supporting documentation all contain information that people may need to identify, classify, verify, and transfer into another process.<\/p>\n\n\n\n AI can change the economics of that work.<\/p>\n\n\n\n Document intelligence can help extract and classify information and make it available to the people and workflows that need it. Instead of asking an employee to find information buried inside a document and manually enter it somewhere else, the technology can support the first stages of that process while people remain responsible for review and exceptions where appropriate.<\/p>\n\n\n\n This is less dramatic than some of the claims surrounding generative AI. It is also much closer to the work that finance organizations perform every day.<\/p>\n\n\n\n And its value compounds. A faster credit decision accomplishes relatively little if documentation then creates another bottleneck before funding. Improving one task is useful. Improving the flow of information across the transaction creates a larger operating advantage.<\/p>\n\n\n\n This brings us to one of the more important findings in Solifi’s research.<\/p>\n\n\n\n Equipment finance respondents placed integration capabilities and API readiness among the leading considerations when selecting technology providers. Source: Solifi Blog<\/a><\/p>\n\n\n\n There is a reason integration and AI are increasingly part of the same conversation.<\/p>\n\n\n\n An equipment finance transaction can cross originations, credit, documentation, funding, servicing, and portfolio management. If those functions operate across disconnected environments, introducing an AI capability at one point in the process does not eliminate the underlying fragmentation.<\/p>\n\n\n\n In some cases, it can simply create another point solution that needs to be integrated and governed.<\/p>\n\n\n\n The stronger opportunity comes from connecting execution, data, and intelligence.<\/p>\n\n\n\n Transactions need reliable workflows through which work can be executed. Data needs to be accessible across the lifecycle rather than trapped inside individual applications. Intelligence can then operate against that foundation, helping people interpret information, identify exceptions and determine what needs attention.<\/p>\n\n\n\n This is where AI begins to move beyond isolated productivity gains and becomes part of the operating model.<\/p>\n\n\n\n AI adoption also introduces legitimate questions about control, accountability, and risk.<\/p>\n\n\n\nFollow the friction<\/h2>\n\n\n\n
Credit decisioning is an obvious starting point<\/h2>\n\n\n\n
Documentation may be one of the most immediate opportunities<\/h2>\n\n\n\n
AI cannot solve disconnected workflows by itself<\/h2>\n\n\n\n
Governance needs to develop alongside capability<\/h2>\n\n\n\n