Claims workflow automation for a UK insurance group

Context and constraints

The insurance group handled a high volume of claims across several lines of business, but the process for triaging and progressing those claims had not kept pace with the organisation's growth. Claims arrived through multiple channels, were keyed into systems that did not talk to one another, and were then triaged largely by hand. The consequences were predictable: avoidable delays as work queued behind manual steps, leakage where claims were settled less accurately than they should have been, and a customer experience that felt slow and opaque at exactly the moments when customers most needed reassurance.

The constraints shaped the solution. Insurance is a regulated domain in which fairness, auditability and consistent treatment of customers matter a great deal, so any automation had to be explainable and reviewable rather than a black box. The group also could not afford to pause claims handling while systems were replaced; claims keep arriving regardless of any transformation programme. And there was a strong, well-founded insistence that automation should support adjusters rather than attempt to replace their judgement, particularly on complex or sensitive claims.

The approach in depth

We began by mapping the end-to-end claims journey, from first notification through triage, assessment, decision and settlement, and we instrumented it to find where time and value were actually being lost. This evidence-led start mattered because intuition about bottlenecks is often wrong; the data showed that much of the delay came not from genuinely difficult decisions but from routine handovers, re-keying and waiting for information that the organisation already held.

Armed with that picture, we designed a workflow automation layer that orchestrated the claims process across the existing systems rather than ripping them out. The automation took ownership of routing, prioritisation and the assembly of relevant context, so that when a claim reached a human it arrived with the right information already gathered. Straightforward, low-risk claims could be progressed automatically against clear rules, while anything ambiguous, high-value or sensitive was routed to an adjuster with a recommendation and a full explanation of how it had been reached.

We were careful to keep a human firmly in the loop wherever judgement mattered. Every automated decision was logged with its rationale, and adjusters retained the ability to override outcomes, with those overrides captured as feedback that improved the rules over time. This kept the process fair, auditable and trusted, both by regulators and by the adjusters themselves, whose buy-in was essential to adoption.

Delivery phases and sequencing

We delivered in slices aligned to claim types rather than attempting to automate everything at once. The first phase targeted a single, well-understood and high-volume claim category, where the rules were clear and the risk of getting it wrong was low. Running the automation alongside the existing manual process let us compare outcomes and tune the rules with confidence before letting automation take the lead.

Later phases extended the approach to additional claim types and progressively shifted more of the routine work onto the automation layer, always retaining human oversight for exceptions. Sequencing the work this way meant the business saw value early, the change was absorbed gradually by the operational teams, and we never bet the entire claims operation on a single release. Each phase concluded with a review of outcomes against fairness and accuracy measures, ensuring that efficiency gains never came at the expense of good customer outcomes.

Architecture and technology decisions and trade-offs

We favoured an orchestration-led architecture that integrated with the group's existing claims systems through well-defined interfaces, rather than a rip-and-replace platform. This decision traded a little architectural elegance for a great deal of safety and speed: by leaving the systems of record in place and coordinating them from above, we avoided a large, risky data migration and delivered value far sooner. The automation layer became the brain of the process while the existing systems remained the source of truth.

For the decisioning itself, we deliberately preferred transparent, rule-based logic for the core of the triage process, reserving more advanced techniques for narrow, well-bounded tasks such as classification of incoming documents. The trade-off here was conscious: a more aggressive use of opaque models might have automated a few more cases, but at the cost of explainability and regulatory comfort that the group rightly valued more. We also designed the system to fail safe, defaulting to human handling whenever confidence was low or a case fell outside known patterns, on the principle that an unnecessary human review is a far better outcome than an unfair automated one.

Measurable outcomes

The automation removed a great deal of avoidable delay from the process. Routine claims that had previously waited in manual queues began to progress promptly, and adjusters were freed to spend their time on the complex cases where their expertise genuinely added value. We typically see cycle times for straightforward claims fall substantially once routing and context-gathering are automated, and this engagement followed that pattern.

Leakage also improved, because consistent rules and assembled context reduced the variation and oversight errors that creep into purely manual handling. Perhaps most importantly, the customer experience improved: claims moved faster, customers were kept better informed, and the moments of genuine human contact were reserved for the situations that warranted them. The fact that every automated decision carried a clear, logged rationale meant the group could demonstrate fair and consistent treatment to its regulators with confidence.

• Evidence-led process mapping to target the real sources of delay rather than assumed bottlenecks.
• Orchestration over replacement, coordinating existing systems rather than undertaking a risky rip-and-replace.
• Human-in-the-loop design keeping adjusters in control of complex, sensitive and high-value claims.
• Explainable, rule-based decisioning with every outcome logged and reviewable for fairness and audit.
• Fail-safe defaults routing any low-confidence case to a human rather than forcing an automated decision.
• Phased rollout by claim type delivering value early while limiting the blast radius of change.

Lessons learned

The strongest lesson was that, in a regulated and human-centred process, trust is the real currency. By making automation transparent and keeping adjusters in control, we earned the adoption that ultimately determined the programme's success; a more aggressive, opaque approach would very likely have been resisted and undermined regardless of its theoretical efficiency. A second lesson was the power of automating the unglamorous parts: much of the value came not from clever decisions but from eliminating re-keying, handovers and waiting.

We were also reminded that efficiency and fairness need not be in tension when the system is designed thoughtfully. By measuring outcomes against fairness and accuracy alongside speed, the group could pursue faster handling with the confidence that it was not quietly degrading the quality of decisions. The result was a claims operation that was faster, more consistent and more humane, all at once.

If manual triage and fragmented systems are slowing your claims and harming customer experience, we can help you automate them safely and transparently. Talk to us about a similar engagement. Email sales@halfteck.com.