AI use cases in insurance operations: the work either side of the claim

The bottleneck in most insurance operations is not where the org chart says it is. It is the endorsement queue on a Tuesday afternoon: forty broker emails, each asking to add a vehicle, uplift a sum insured or note an interested party, each needing somebody to open a terminal session, find the policy, key the change, recalculate the premium and send a confirmation that will itself generate a query. It is the suspense account holding broker remittances that tie to no policy reference anyone can find. It is the coverholder bordereau whose columns moved again this month.

Claims and underwriting get the attention and the budget, and both are covered elsewhere. This article is about everything else, which is where most of your headcount sits. The pattern is the same in each process: the agent reads what arrives, normalises it against what your systems hold, makes a bounded decision inside limits you set, writes structured output back into the system of record, and hands anything ambiguous to a named person with the reading already done.

Submission intake, underwriting and claims, briefly

A submission lands as a broker email with a signed proposal form, a schedule of locations or vehicles in a spreadsheet nobody formatted, and five years of loss runs in three layouts. An intake agent reads the lot, works out whether this is a new risk or the same risk from a second broker, and produces an appetite decision or a clean underwriting file with the gaps listed. The same mechanism runs from first notification on the claims side. Neither is where you should start.

Policy administration and the endorsement queue

Mid term adjustments are the highest volume decision your operation makes and the least designed. Requests arrive as free text email, portal messages, change forms scanned at an angle, and spreadsheets where a broker has highlighted three rows in yellow and written "please action".

An agent reads the request and identifies the policy, which is harder than it sounds when the broker quotes a quote reference rather than a policy number, or a trading name that differs from the legal entity on the schedule. It classifies the change, checks it against the delegated authority and product rules, calculates the pro rata premium including insurance premium tax, drafts the wording from your approved clause library, writes the transaction into the policy administration system and produces the endorsement document and updated schedule.

The exception path

A new location in a flood zone, a change of occupancy that is really a new risk in disguise, an addition backdated past your rules: those go to an underwriter with the reason for referral set out.

Billing, premium collection and cash allocation

Cash allocation is where insurance operations quietly loses money. A broker pays one amount covering sixty policies net of commission, sends a remittance advice as a spreadsheet or a scanned statement, and the bank feed shows a single credit that matches nothing.

An agent reconciles it line by line: parse the remittance, match each line to a policy or instalment, handle the commission deduction that does not match your agreed rate, spot the credit note the broker applied without telling you, and propose the cash application for approval rather than posting it directly. Unmatched residue comes back as a specific query rather than a suspense entry that ages for ninety days. The same agent runs collections mechanics: instalment plans, failed direct debits, arrears per broker account, chase correspondence referencing the outstanding items.

The line at cancellation

Cancelling for non payment is regulated on notice period and content, and a wrongly issued lapse notice is both a conduct problem and a coverage dispute. The agent prepares the case; a person issues the notice.

Reinsurance bordereaux and cessions

If you run delegated business, your data quality problem has a name and it is the monthly bordereau. Risk and claims bordereaux arrive from each coverholder in their own template, with headers that drift between months, mixed currencies, date formats that swap day and month, and mandatory fields left blank because their system does not capture them.

An agent maps each file to your canonical schema, holding a learned mapping per coverholder rather than a brittle fixed template, validates every row against the binder terms, and flags risks written outside the agreed limits, territories, occupancies or line size. It reconciles reported premium against cash received, ties the claims bordereau to paid and outstanding amounts, and applies the treaty structure to produce cessions for quota share, surplus or excess of loss layers. It writes back a normalised file, an exception report per coverholder, and a drafted query listing which rows need fixing. The conversation about a binder breach stays with your delegated authority manager.

Regulatory and conduct compliance evidence

Compliance work in insurance is mostly evidence assembly. Fair value assessments, target market reviews, claims handling timeliness, treatment of vulnerable customers, sanctions screening records, delegated authority audits. The judgement is a small part of it; reading several hundred files to find out what happened is the rest.

An agent does that reading. For a fair value assessment it pulls the sampled policies, the premium and commission split across the distribution chain, claims frequency and acceptance rates, complaints and cancellation patterns, and drafts the assessment with every statement cited back to its source record. Sign off stays with the compliance officer. The gain is a bigger sample, checkable citations, and evidence that can be regenerated when the regulator asks a follow up question a year later.

Complaints handling

Under recording is the finding that comes up again and again, because a complaint often arrives as a sentence in the middle of an ordinary email, or as a broker forwarding something with "can you look at this". An agent reads inbound correspondence and call notes across channels and applies your definition consistently, which beats judgement made at speed.

It then opens the case with the correct clock running, assembles the file from the policy, claim, billing and correspondence history, identifies the root cause category, and drafts the final response setting out what happened, what the policy says, what is offered and the onward referral rights. It flags vulnerability indicators and cases whose facts resemble ones an ombudsman has upheld before. Nothing goes out unreviewed. Root cause reporting, usually too late to be useful, becomes a live view of which product or process is generating the pain.

Broker and agent servicing

The servicing inbox is repetitive and latency sensitive. Brokers want a copy of a schedule, an employers liability certificate, a cover note, a claims experience summary, confirmation a premium was received, or a status on a quote. Almost every answer already exists in a system. An agent answers the unambiguous ones, issues the standard documents, and drafts the rest with supporting data attached. It also handles agency onboarding: checking the terms of business agreement, confirming regulatory permissions, and assembling the due diligence file your next audit will ask for.

Renewals and retention

Renewal work fails on timing, not on skill. Invitations go out late because pre renewal data collection was late. An agent starts the cycle at the right distance, builds the renewal file, and identifies what has changed: exposure movements, claims since last renewal, endorsements that never made it into the base schedule, wording differences between the expiring contract and current standard terms. It chases the broker for updated declarations with a list of what is missing, and prepares the invitation pack. On retention it surfaces the early signals that a policy is at risk: a declined endorsement, a complaint, arrears, a sharp exposure reduction, a broker who has stopped replying. Those go to a person, because keeping an account is a relationship task.

Actuarial and data preparation

Before a reserving exercise happens, somebody spends weeks reconciling claims and premium extracts against the ledger, explaining movements between quarters, rebuilding triangles after a coding change, and writing up data quality caveats. An agent reconciles, explains variances by tracing to transactions, normalises historic coding and documents every adjustment. The line is absolute: it does not select assumptions, set reserves or sign anything.

The legacy system problem

Most of this runs into a policy administration system written decades ago, reached through a terminal emulator, with no usable interface and a vendor quote that assumes a two year programme. You do not need to replace it first. Integrate in this order: read from nightly extracts and the database directly if you can get access, write through whatever batch or file interface already exists, and use screen level automation only where nothing else is available. Screen automation belongs in the plan as a temporary bridge with alerting on every step, not as an architecture. The real advantage agents bring is tolerance for the mess: they can reason about a product code nobody has documented since the person who wrote it retired.

Where to start

Pick a process that is high volume, has an unambiguous right answer and fails visibly. Cash allocation and bordereau normalisation are the usual first candidates, because you can compare the output against what your team produced last month before it touches anything. Run it in shadow mode, give write access only behind explicit authority limits, and log every decision in a form internal audit can replay.

Where AI fails here

These systems fail in ways worth knowing before you commit budget. They fail on policy wording at the edges, where the answer depends on precedent, market practice and what the underwriter intended, none of which is in the document. They fail when a broker's instruction is genuinely ambiguous, and a confident guess on a sum insured is worse than a query.

They fail on data that is wrong rather than missing: an agent will happily reconcile to a coverholder's incorrect figure and produce a clean looking exception report. They fail on legacy integrations that change without notice, which is why screen automation needs monitoring as if it were an unreliable third party, because it is one. They inherit whatever bias sits in your historic data, so any agent influencing referral, pricing or collections treatment needs testing for disparate outcomes before and after go live. And they fail when deployed as a black box: the first time an operations lead cannot see why a premium adjustment was applied, the team routes around it.

Narrow scope, cited reasoning, hard authority limits, human ownership of consequential decisions, continuous measurement. If you want a straight assessment of which parts of your operation an agent should own and which it never should, describe the process to Adopt AI Partners and we will tell you honestly.