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Fintech & Insurance Agents · Cluster 1 of 4

Claims & recovery

From the first notice of loss through to subrogation and repair — answering every FNOL call under catastrophe-load, surfacing overlooked recovery opportunities, and keeping the repair loop moving. Where cycle time and leakage live.

Use case 401

First Notice of Loss (FNOL) intake

The pain point

FNOL is where the claims experience begins — and where it most often fails under load. Insurance calls already run long, and during a regional catastrophe event volumes spike suddenly, pushing hold times up and abandonment past the point where customer satisfaction falls away.25 The routine part of intake — policy verification, date, time, description of incident — is exactly the repetitive work that clogs the queue.

Impact — organisation

Queues and abandonment spike precisely when demand peaks (CAT events); skilled adjusters tied up on routine data capture; a poor first impression at the worst possible moment.

Impact — customer / client

A distressed policyholder — just after a loss — left on hold when they most need to get through.

How it’s addressed

A voice agent answers every FNOL call immediately, captures the structured intake data (policy details, incident description, time and place) and routes the claim, handing the caller to a human adjuster for the high-empathy confirmation. It scales instantly during a surge, absorbing concurrent calls without a queue.

The benefits
Organisation

Designed to remove hold-time abandonment during demand spikes and to free adjusters to focus on the empathetic, judgement-based part of intake rather than data capture.

Customer / client

Getting through immediately after a loss, and reaching a person for the part that needs one.

Use case 405

Post-claim recovery & subrogation triage

The pain point

Money leaks out of claims quietly. EY’s claims practice estimates financial leakage at 7–14% of carriers’ total claims spend,46 and a large share of that is missed recovery: around 15% of P&C claims are closed without spotting a valid subrogation opportunity, an estimated $15–20bn a year industry-wide.47 Busy adjusters, measured on cycle time, miss the subtle third-party-liability signals in unstructured text.

Impact — organisation

Recoverable money left on the table; leakage that never shows on a P&L line; adjuster caseload too high to catch every subrogation signal.

Impact — customer / client

For the insurer’s wider book, higher net loss costs — which ultimately feed premiums.

How it’s addressed

A back-end agent reads across claims narratives and third-party report text — locally, behind the firewall — and flags files with likely third-party liability to the recovery team, restricting alerts to high-probability targets so adjusters are not swamped with noise.

The benefits
Organisation

Designed to surface overlooked subrogation opportunities the recovery team can then pursue, while protecting adjuster capacity by flagging only high-probability files.

Customer / client

A fairer overall book, with recoverable costs pursued rather than quietly absorbed.

Use case 409

Contractor & repair-network orchestration

The pain point

Claims cycle time — from approval to final vehicle or property restoration — commonly runs 14 to 21 days, and every extra day a vehicle sits in a garage adds courtesy-car or loss-of-use cost, typically £35–50 a day, that the insurer carries.46

Impact — organisation

Long cycle times inflating loss-adjustment cost; courtesy-car outlay accumulating per day of delay; poor visibility of where each repair actually is.

Impact — customer / client

A policyholder waiting weeks, with limited visibility, to get their vehicle or property back.

How it’s addressed

An agent automates the tracking loop between repair shops and the claims platform — chasing milestones, updating status, flagging stalls — keeping the restoration process moving and the insurer informed, so delays are caught early rather than discovered at settlement.

The benefits
Organisation

Designed to tighten the repair-tracking loop and reduce avoidable cycle-time delay, which is where courtesy-car and loss-of-use costs accumulate across a large motor book.

Customer / client

A faster, more visible route to getting their vehicle or property back.

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Sources & references

Statistics describe demand and conditions across the relevant sector and are drawn from the cited public sources. They characterise the sector-level problem these agents address; they are not performance claims for any DVAI product. Deep Voice AI Limited is a pre-revenue company and makes no representation as to outcomes for any individual organisation. Figures are current as at the date of the cited publications.

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