Claims AI Assessment: Take the test now
Claims AI Assessment: Take the test now
PROPERTY
Sprout.ai identifies around 30% more claims that are not covered than the adjuster benchmark, while maintaining consistently high levels of accuracy on covered claims.
Helping insurers identify non-covered claims earlier and avoid paying indemnity that may later need to be reversed.
Overview
Property claims often involve complex policies, endorsements and coverage changes, making accurate coverage decisions challenging. Sprout.ai’s Policy Checking and Coverage AI Agent analyses the policy, endorsements and loss details to recommend coverage decisions grounded in the terms that apply.
Integrated into existing claims systems, it provides transparent, explainable recommendations that adjusters can review and approve, helping reduce errors, prevent leakage and improve the policyholder experience.
The challenge
Benchmarked against a gold-standard dataset, adjusters of differing tenure reached the correct outcome on up to 98% of covered claims. Coverage accuracy is not the problem. Finding the claim that is not covered is, and up to 30% of them are paid incorrectly.
Each customer may hold multiple policies, with up to 30 endorsements beneath them, and every one has to be considered against the loss reported.
A cause of loss may be excluded while the resulting damage is covered. Assessing both separately is essential for accurate property claim coverage decisions.
FNOL descriptions are often brief and lack key details. Accurate coverage decisions may require further evidence or investigation before confirming coverage.
Your most experienced adjuster and your newest adjuster should reach the same answer on the same claim. In reality, high claim volumes and time pressure leave less time to review the detail, which can lead to different judgements or important details being missed.
The same claim can produce different outcomes depending on who reviews it. In reality, there is volatility in judgement calls like these. Depending on adjuster experience and potential assumptions, the decision may differ.
sprout.ai understands and interprets
| Claim dimension | What Sprout.ai contextualizes | What it returns |
|---|---|---|
| Policy bundle | The document payload as it arrives,declaration pages, endorsement documents and core policy wording, across the policy and its renewals | What each document is, the policy type, the coverages and limits stated on the declaration page, the endorsements in scope, and whether anything further is needed to reach a judgement |
| Prevailing document | Multiple policies per customer, renewals, and up to 30 endorsements beneath each, against the date of loss | Which document governs this claim, which endorsement overrides the base form, and why that one prevails |
| Coverage | Declarations, endorsements and riders, against the loss described | Whether the loss is covered or excluded, and the clause that says so |
| Limits and cost-sharing | Coverages A, B and C, sub-limits, deductibles | The limits that apply, and any sub-limit that caps the outcome |
| Loss evidence | FNOL notes, loss and damage descriptions, and supporting documents | Cause of loss, damaged items and place of occurrence |
| Cause and consequence | The peril claimed, against the damage described | Which part of the loss is excluded at source, and which is covered as ensuing damage |
| Policy period | Policy dates, renewals, cancellation and reinstatement history | Whether the loss occurred inside a covered period, and which coverages were in force during it |
| Completeness | What was submitted, against what the coverage decision needs | Whether there is enough to make a coverage decision, or what is missing and who supplies it |
| Recommendation | All of the above, taken together | The recommended coverage position, the governing clause, and the reasoning that connects them |
How sprout.ai improves claims performance
Takes the document payload as it arrives and works out what it is holding. Classifies every document into declaration pages, endorsements and core policy, then reads the declaration page for the policy type, the coverages and limits, and the endorsements in scope.
Reads the loss description and FNOL data and establishes what happened including loss cause, damaged items, place of occurrence, then decides whether there is enough here to reach a coverage position at all.
Identifies the applicable policy and endorsements, applies exclusions, sub-limits and deductibles, distinguishes excluded causes from covered damage, and delivers clear, explainable property insurance coverage recommendations.
Delivers a clear coverage recommendation, supporting policy documents, applicable clauses and traceable reasoning directly within Guidewire or your existing claims management system.
Sprout.ai recommends; your adjuster decides. Every claim carries a manual touch point today, and in this deployment that was a state regulatory requirement.
Every disagreement is captured as structured feedback and reviewed for systematic gaps, not just logged. New perils onboard on a handful of reference claims, tested against your adjusters’ judgement in a non-production environment, then enabled one at a time in production.
Integrates with your existing claims system, returning recommendations and reasoning without replacing systems or changing workflows.
The KPIs sprout.ai moves
I had the opportunity to lead the implementation at MetLife Chile, working alongside the Sprout.ai team. It was a great experience of collaboration and innovation, delivering excellent results for customer experience.
Pablo Guajardo
|
Claims Manager & Operational Transformation Lead, Metlife Chile
AdvanceCare is continuously looking for innovative ways to improve the insurance claims experience. We selected Sprout.ai as our partner, as they have demonstrated that their technology and approach can take us one step forward in our digitalization and efficiency journey.
Sergio Melro
|
Chief Operating Officer, AdvanceCare
Our partnership with Sprout.ai is another example of how MetLife is innovating with high tech solutions to address real customer needs.
Venkata Natarajan
|
Chief Information Officer, MetLife
Our customers highly value the digital reimbursement solution that we provide through our app and web portal. Focusing on efficiency also means focusing on time resolution and the quality of service we provide to our customers. This is why we see AI as a useful tool and look for solutions that contribute to this, such as Sprout.ai.
Liliana Silva
|
Operations Director, AdvanceCare
We know making a claim can be stressful, so we're dedicated to processing them quickly and smoothly. By partnering with Sprout.ai, we're using cutting-edge AI and NLP to free up our claims handlers, allowing them to focus on delivering empathetic support to customers.
David Russell
|
Senior Manager, Claims & Underwriting Strategy, Scottish Widows (Lloyds Banking Group)
Sprout.ai can capture much more data, then format it so it is structured, allowing analysis into needs, leakage, and care quality.
Joanne Richardson
|
Former Health Director, AXA
ai for health claims
Is AI actually better than my adjusters at coverage?
On the decisions that cost money, the evidence says yes. Benchmarked blind against a gold-standard reviewer, three adjusters of differing tenure identified 52–64% of non-covered claims. Sprout.ai identified 76–81% of the same claims, around 30% more. It also applies the same standard to every claim, regardless of tenure or caseload.
Why does non-coverage detection matter more than speed?
Because the error is one-directional. A claim wrongly paid is indemnity gone; a claim wrongly denied gets appealed and corrected. Adjusters are already accurate on covered claims — 94–98% in the benchmark. The gap is on the other side, and closing it goes straight to loss cost rather than expense.
Can AI genuinely read endorsements?
That is the core of it. A customer may hold multiple policies with up to 30 endorsements beneath them. Sprout.ai identifies which document governs the loss on the date it occurred, then applies its terms, including the service-line, hidden-mould and limited-fungi style endorsements that reverse or cap what the declaration page alone would give.
Does automating the coverage check mean AI decides the claim?
No. Sprout.ai returns a recommendation with the governing document, the clause applied and the reasoning. The adjuster agrees or disagrees and makes the decision. In this deployment, human review on every claim was a state regulatory requirement and the workflow was built to it.
What happens when the loss description is not clear enough to call?
Sprout.ai triages before it decides. Where the FNOL does not contain enough to reach a coverage position, it returns more information needed and names what is missing, rather than producing a covered-or-excluded answer the evidence cannot carry. Recall on that step was 99.4% across six months of production.
How does Sprout.ai support oversight and regulatory review?
Every recommendation records the documents considered, the clause applied, the reasoning, the role of Sprout.ai and the role of the adjuster. That gives you a traceable record of who did what, why and when — so any outcome can be reviewed, explained to a policyholder, or reconstructed for an internal, state, NAIC or European regulator.
What happens during a catastrophe surge?
The coverage check does not compete with headcount. Monthly volume in the pilot ranged from 78 to 753 claims, and accuracy improved as volume rose rather than degrading with it — the opposite of what manual review does under surge.
How quickly can Sprout.ai integrate with our existing claims management system?
Sprout.ai connects via API to existing claims infrastructure, including Guidewire, Duck Creek and other platforms, without core system replacement. In this deployment it runs inside Guidewire ClaimCenter: claim data and document bundles in, recommendation to the adjuster and a note written back. Sprout.ai’s Guidewire Cloud Accelerator is available now.
