The complaints function is one of the few places in a regulated business where a single mishandled interaction can become a reportable failing. Yet complaint handling does not usually get top billing in conversations about AI in financial services. Fraud detection, agentic commerce and AI-driven lending draw most of the attention (and budget).

Having worked alongside regulated industries for a long time, my view is that AI tools can give complaint teams the capacity to handle rising volumes better, without trading away the quality of service that authorities and customers expect.

Technology should read complaints the way a person would

The first advantage of AI in complaint handling comes before a case is even opened, in how an issue is recognised and captured in the first place. A traditional complaint management system built on fixed fields can only record what someone has already classified as a complaint and keyed into the right boxes. The large language models (LLMs) behind AI-driven complaint handling work the other way round: they read the message itself, reviewing what the customer means rather than matching the words they happen to use.

Customers seldom word their grievances in the language your CRM expects. They may write three frustrated paragraphs without ever using the word “complaint”, bury the real issue in a message about something else, or signal their distress through tone rather than content. A fixed-field system has no way to catch any of that. AI understands intent and can tell a complaint apart from a general query.

An intake agent can monitor every channel, recognise a complaint from its content, extract the key points, and open a case. Your case handlers then starts from a structured summary instead of reconstructing events from a long email thread or multiple back-and-forth conversations.

The value continues once the case is open

Capturing a complaint accurately is only the beginning. The greater part of the work falls across the days and weeks that follow, and AI remains valuable throughout the process.

A case handler's expertise lies in investigation and decision-making, yet a considerable share of their time is spent composing correspondence: the acknowledgement, the holding letter, and then the final response. AI changes that balance, drawing on data and approved templates to prepare a first draft setting out the issue, findings, the decision and the next steps, in your firm's approved tone. The handler can still review and approve this correspondence, but the labour of producing the letter becomes automated.

AI tools are also attuned to the subtleties of customer communications. They can detect signs of customer vulnerability, such as the mention of a change in circumstances, or language suggesting the customer is struggling to cope. Flagged at the point of intake, these indicators shape how a case is handled — the pace and tone of correspondence, the priority it is given, and whether it warrants more experienced oversight — ensuring your firm meets its commitments to treating customers fairly.

Accountability holds as cases move between people

AI also has a critical part to play in how cases are managed as they pass between people; a point at which your firm's line of accountability is most exposed. When a case is reassigned mid-investigation, or picked up by a colleague returning from leave, the detail behind it — the context, commitments already made, and the reasoning behind a decision — can be lost in the transfer.

AI agents can assess each case against the experience and current workload of available handlers, and the particular nature of the complaint. A complex or sensitive matter, including one flagged for vulnerability, is given to senior agents, while routine cases are distributed among the wider team. The team leader retains full oversight and can reassign or override any decision.

Continuity is also preserved through the handover process. Because AI maintains a summary of each case, which refreshes as it develops, a colleague taking on the file inherits an accurate summary rather than weeks of notes to interpret. Your complaints function is able to absorb fluctuating volumes without the quality of service becoming a casualty.

The standard of handling can be evidenced

Handling complaints to a high standard is one requirement; demonstrating that standard to a regulator or auditor is another. Quality assurance is the established means of providing that evidence, but reviewing a case in the necessary depth takes time, so most QA programmes can examine only a fraction of your caseload. The fraction selected are also rarely weighted towards the cases that most warrant scrutiny.

AI eases that constraint considerably. Rather than relying on a reviewer to work through a sample, it can monitor the entire caseload and identify the cases that merit closer attention, such as those approaching a regulatory deadline, or those exhibiting the characteristics associated with higher risk.

For the cases that proceed to formal review, AI t can pre-populate the answers to the standard QA questions from the case record, leaving the reviewer to confirm, amend and sign off each assessment. And because every action is logged, your firm is left with a complete and auditable account of how each case was handled, when, and by whom.

Your choice of tool is part of the decision

At each stage, AI takes on the work that consumes a handler's time — the monitoring, the drafting, the allocation, the assembling of evidence — freeing them to focus on higher skilled, judgement-based work. For a regulated firm, the result is a complaints operation that is more responsive to customers and more accountable to industry authorities.

However, the value that AI delivers ultimately depends on the tools your firm chooses, and whether its capabilities are integrated within your complaints management software. If this is a route you are weighing up, it is worth asking technology providers whether their AI has been designed for the demands of regulated complaint handling or adapted from a system intended for something broader.

At Aptean, we have spent more than thirty years working alongside complaint handling teams in regulated industries, and that experience shapes how we are bringing AI to the Respond platform. It is designed into each stage of the case lifecycle, with the demands of a regulated operation accounted for throughout. We take this approach because the pressures on complaint handling are only increasing, from customers and regulators alike. The technology will keep advancing; the bar will not drop.

Martin Canwell is a Senior Account Manager at Aptean Respond, whose platform has supported complaint handling teams in regulated industries for more than 30 years.

Aptean Respond’s new ebook, five steps to using AI in your complaints operation, is available to download now. Five-Step Guide to Using AI in Your Complaints Operation | Aptean Respond

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