It’s 11:30 at night and a borrower opens a lending app to find out why their loan application was declined. No one on the support team is online. The borrower still expects an answer.
That gap between borrower expectations and business hours is one of the reasons AI-powered customer support has grown so quickly across financial services.
Lenders are dealing with more customers, more complex queries, and a borrower base that now expects the same instant service they get from ride-hailing apps and e-commerce platforms. Meanwhile, building a customer support team large enough to handle all of that, across every time zone, around the clock, is expensive and operationally difficult.
But customer support in lending carries more weight than in most other industries. When a borrower reaches out, it’s often because something has gone wrong, a payment failed, a loan was declined, a fraud alert appeared, or a direct debit needs to be disputed.
These aren’t simple situations. They involve money, stress, and trust. Getting the response wrong has real consequences, not just for customer satisfaction, but for the lender’s reputation and regulatory standing.
The question isn’t really whether to use AI or human agents. The more useful question is which situations call for which, and how to build a support operation that handles both well.
Why this has become a pressing issue
AI adoption in customer service has grown quickly, and the numbers back it up. Electroiq’s AI Customer Service Statistics report shows that 89% of contact centers now use AI chatbots, and businesses that have deployed AI for support report a 37% shorter first response time and 52% faster ticket resolution on average.
Klarna is one of the most cited examples: the company reported that its AI assistant does the equivalent work of 700 full-time agents and delivered an estimated $40 million improvement in annual profit, as reported by NexGenCloud’s customer service cost analysis. Numbers like these have made a lot of lenders feel that AI is a straightforward win.
The reality is more complicated. BlueTweak’s analysis of AI chatbot deployments found that around 40% of customers abandon chatbot interactions because of poor experiences, and every abandoned conversation usually leads to a follow-up contact that costs more to handle than the original one would have.
AI works well when it’s used for the right things: routine, repetitive, low-stakes queries. When it gets pushed into complex or emotionally charged situations, it tends to make things worse, leaving customers more frustrated than before and requiring human agents to start the conversation from scratch.
For lenders, the stakes are higher than in most other industries. A wrong answer about a repayment date, a loan balance, or a collections process isn’t just a bad customer experience.
It can create compliance problems, damage trust when a borrower is already stressed, and push customers to disengage from managing their repayments altogether, which eventually shows up in the loan portfolio.
What modern AI chatbots can actually do
It’s worth being clear about what AI chatbots in 2026 actually are, because they’ve come a long way from the rigid menu-based systems that gave early chatbots a poor reputation.
Modern systems use large language models and natural language processing to understand what a customer is actually asking, rather than waiting for them to pick from a fixed list of options. They can pull up live account data, follow the context of a conversation, and respond in a way that feels more like a real exchange than a scripted interaction.
A well-built lending chatbot can handle things like account balance checks, repayment schedule questions, application status updates, basic eligibility queries, document upload guidance, and simple account changes like updating contact details. That covers a real portion of daily support volume.
Most support teams will tell you that a large share of their incoming tickets are variations of the same handful of questions asked over and over. Automating those lets human agents focus on the conversations that genuinely need their attention.
The availability benefit is also real for lending in particular. Borrowers don’t plan their financial worries around business hours. Application updates, repayment reminders, and account alerts come through at all hours, and being able to give an immediate response, even a basic one, is a better experience than making someone wait until morning for an answer to a simple question.
Read more: The 3 best Freshdesk alternatives for customer management
Where AI consistently falls short
The problems with AI in customer support are well known, and they tend to hurt more in lending than in most other contexts.
The first issue is that AI can sound confident even when it’s wrong.
A system that gives an authoritative-sounding but incorrect answer about a payment deadline, a fee structure, or what happens after a missed repayment can cause real damage.
A human agent can say “let me check that for you.” Some AI systems instead fill the gap with a plausible answer that isn’t accurate, and the borrower acts on it. Lenders need to be clear-eyed about this risk and make sure queries that require precise, verified answers get escalated to people who can give them.
Emotional situations expose the limits of AI most clearly. When a borrower is stressed, scared, or frustrated, they usually aren’t just looking for information. They want to feel that someone understands what they’re dealing with and can help them figure out what to do next.
A borrower who missed a payment because of a job loss or a health emergency needs a response that acknowledges the difficulty, lays out the real options, and offers some room to work with. AI can produce a version of that response, but it doesn’t have the judgment to sense when a conversation is turning serious or when the usual script no longer fits.
Human agents read hesitation, frustration, and confusion in real time, and they adjust. That ability to adapt matters a lot when a borrower is deciding whether to keep engaging with a lender or quietly disappear.
Edge cases and unusual situations are another consistent problem. Lending generates them constantly: a payment taken twice, account records that don’t match what a borrower is showing, a repayment arrangement that doesn’t fit the standard options, a fraud dispute that needs investigation across multiple systems.
These situations call for someone who can think through the context, make a judgment call, and find a resolution that actually works. AI tends to handle them poorly, and the damage from a badly handled complex case is often worse than the original issue.
The real cost of getting this wrong
A lot of lenders adopt chatbots to cut costs, and they focus on the headline numbers without thinking through what happens when the system doesn’t perform well.
The setup costs alone, including software, integration, infrastructure, security, and ongoing maintenance, often come in higher than expected. But the bigger costs are the ones that are harder to see at first.
Regulatory risk is one of them. Financial regulators are paying increasing attention to how lenders handle automated customer interactions, especially when it comes to complaints, collections, and how borrowers receive information about their credit agreements.
A chatbot that gives borrowers wrong information, or that makes it difficult to reach a human agent, is a regulatory problem, not just a customer experience problem.
Trust is harder to measure but just as important. NextPhone’s analysis of AI customer service data found that 64% of customers would prefer companies didn’t use AI for customer interactions at all, even though many of those same people are perfectly happy to use a chatbot for a simple question.
That’s not a contradiction once you understand what people are really saying: they want speed and convenience for easy things, and they want a real person for anything that matters. When lenders push AI into situations where customers want human support, the trust damage that follows is hard to repair.
A practical way to decide what to automate
Lenders that get real value from AI support share a common starting point: they’re honest about what the chatbot can actually handle reliably, rather than deploying it as broadly as possible and seeing what sticks.
The first step is looking at real support ticket data to identify which queries come in most often and which ones are genuinely straightforward. Loan balance inquiries, repayment date questions, application updates, and basic account changes are usually good candidates for automation. Disputes, complaints, hardship requests, fraud cases, and anything touching collections or legal obligations usually aren’t.
The escalation design matters just as much as what gets automated. Every borrower who talks to a chatbot needs a clear, easy path to a real person if they want one.
When customers feel stuck in an automated loop with no way out, their frustration carries into the entire relationship with the lender. The route to a human agent needs to be obvious, quick, and actually functional. A chatbot that sends someone to a 40-minute wait queue hasn’t solved anything.
Measuring the right things is what separates lenders that genuinely improve from those that deploy AI and assume it’s working. Response speed is easy to track but doesn’t tell you much.
The numbers worth watching are resolution rates (did the customer’s issue actually get solved), escalation rates (how often the chatbot had to hand off), customer satisfaction scores for AI interactions specifically, and how many customers come back with the same unresolved issue after a chatbot interaction.
The system also needs regular updates as products change, regulations evolve, and new types of questions start coming in.
What works in practice
The lenders with the strongest customer support use AI and human agents together, with clear thinking about which situations go where, rather than treating them as alternatives to each other.
AI takes the volume: routine questions, account checks, after-hours responses, and the first acknowledgment that a borrower’s query has been received.
Human agents take the situations where judgment and empathy actually change the outcome: complaints, disputes, hardship cases, collections conversations, and anything where a borrower is clearly struggling.
The benefit of that split isn’t just lower costs. It’s that human agents, no longer buried under repetitive queries, can give proper attention to the conversations that actually need them.
That improves the quality of those interactions and tends to improve agent morale as well, which has a real effect on how those conversations go.
Lending is built on trust more than most industries. Borrowers hand over personal and financial information and trust that a lender will treat them fairly when things get complicated.
Customer support is one of the main ways that trust is either earned or lost. AI can make the easy parts faster. Human agents are what make the hard parts trustworthy.
Read more: Should I lend to this customer? A guide to risk assessment
Getting the balance right
How much to automate should be decided by what borrowers actually need at each point in the relationship, not just by what costs less to deliver.
For simple, routine interactions, fast AI responses work well and borrowers appreciate them. For complex or stressful situations, pushing customers into automated systems creates problems that end up costing more than the savings were worth.
Lenders who are clear-eyed about what AI can and can’t do, who build proper escalation paths, and who measure real outcomes rather than just cost per ticket, tend to build support operations that work well for borrowers and for the business.
The technology will keep getting better, and AI will handle a growing share of customer interactions over time. What stays the same is that lending is a relationship-driven business, and the moments that matter most in that relationship will keep needing people to get them right.
