Claims AI Assessment: Take the test now
Claims AI Assessment: Take the test now
LIFE
Every claim reaches an adjuster in minutes, with the history reconstructed, the circumstances established and the reasoning cited back to the page it came from.
This is vastly different to the current reality of Life & Protection claims, with some taking 70+ days stuck in multiple adjuster queues.
Overview
Life insurance claims often involve complex medical records, policy definitions and supporting evidence. Sprout.ai’s Life Claims AI Agent analyses claim documents, assesses eligibility against the relevant policy terms and provides clear, explainable recommendations with supporting evidence.
Integrated into existing claims workflows, it helps adjusters make informed decisions, reduce manual reviews and focus on delivering a better experience for beneficiaries.
The challenge
Whether a life insurance claim pays depends on interpreting clinical terms, severity thresholds and policy definitions against the medical evidence. Each decision requires careful assessment of the wording and supporting documents.
Tracing how a condition developed, when symptoms first presented, whether this is a recurrence or a new event, which treatment followed which diagnosis, means reconstructing years of records written by different clinicians for different purposes.
Families may need to provide medical, employment or death records depending on the circumstances. Identifying requirements early helps reduce delays and unnecessary back-and-forth.
Contestability periods, waiting periods and claim deadlines can affect eligibility. Accurate decisions depend on verifying key dates against the evidence and policy terms.
Consistent life insurance claim decisions matter. Sprout.ai helps adjusters review complex, lengthy records more efficiently, supporting thorough assessments and reducing variation between reviewers.
sprout.ai understands and interprets
| Claim dimension | What Sprout.ai contextualizes | What it returns |
|---|---|---|
| Claim file | The document payload as it arrives, claim forms, medical records, certificates, forensic reports, identity and employment documents | What each document is, what it contains, and whether anything needed to reach a judgement is missing |
| Medical history | Years of clinical records, discharge summaries, specialist reports and referrals across the full file | A dated account of what happened ,onset, diagnosis, treatment, progression,with each fact traced to the page it came from |
| Condition and definition | The reconstructed history, against the illness definitions and benefit conditions in the contract | Whether the documented condition meets the definition, and the wording that says so |
| Cause and circumstances | Death certificates, post-mortem findings, police reports and toxicology results | The established cause and type of death, and any circumstance that qualifies or excludes cover |
| Cover and eligibility | Policy dates, waiting periods, contestability windows and statutory limits, against the established date of the diagnosis or death | Whether the claim falls inside cover, and which contractual clock applies to it |
| Membership and employment | Membership records, employment status and leave records, as at the date of the diagnosis or death | Whether the person was covered under the scheme at the time |
| Benefit calculation | Admission and discharge records, periods of care, limits and daily rates | The period that qualifies and the amount it produces |
| Consistency | Every document in the file, read against every other | Where names, dates, identifiers or clinical facts disagree across the record |
| Completeness | What was submitted, against what this particular claim needs | Whether there is enough to decide, or what is missing and who supplies it |
| Recommendation | All of the above, taken together | The recommended eligibility position, the definition applied, and the reasoning that connects them |
How sprout.ai improves claims performance
Analyses clinical records of any length, building a chronological timeline of symptoms, diagnoses, treatments and specialist involvement. Every fact is linked to its source page, with contradictions flagged for review.
The AI agents’ findings resolve into one output per claim: the recommended eligibility position, the definition applied, the evidence considered, and the reasoning that connects them, traceable back to the document and page it came from.
Tests the reconstructed history against the definition that governs the claim, then applies the terms around it including waiting periods, contestability windows, statutory limits, exclusions, and the benefit conditions that determine the amount. Returns the eligibility position, the wording it rests on, and the reasoning behind it.
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
Book a demo to see how Sprout.ai brings policy details and claims evidence together to support better property claims decisions.
ai for health claims
Can AI actually decide whether a condition meets a policy definition?
hat is the core of it. On a life and protection insurer’s own historical critical illness claims, Sprout.ai reached the same eligibility outcome as their claims team on nine in ten, up from around eight in ten before it was tuned to their definitions and to how their team applies them.
How does it handle a medical record that runs to hundreds of pages?
t reads all of it. Sprout.ai reconstructs the record as a dated clinical history – onset, diagnosis, treatment, progression, with every fact tied back to the page it came from, and flags where the record contradicts itself. Handlers spend their time deciding rather than reading.
What happens when the file is not complete enough to decide?
Sprout.ai returns more information needed and names the specific gap. What is required is worked out from the claim itself rather than a fixed checklist – a death from other than natural causes calls for a post-mortem, a death in a road accident calls for a licence and a toxicology result. That means the family is asked once, for the right things, rather than repeatedly.
Does automating the assessment mean AI decides the claim?
No. Sprout.ai returns a recommendation with the definition applied, the evidence considered and the reasoning. Your handler agrees, disagrees, or overrides, and makes the decision. You configure which recommendations require review before they take effect.
How does it handle waiting periods, contestability and time limits?
As part of the eligibility assessment. Sprout.ai establishes the date of the diagnosis or death from the evidence rather than from the claim form, then tests it against the terms that depend on it – waiting periods before an illness is covered, contestability windows, and statutory limits on how late a claim can be brought
How does Sprout.ai support oversight and regulatory review?
Every recommendation records the evidence considered, the definition applied, the reasoning, the role of Sprout.ai and the role of the handler. That gives you a traceable record of who did what, why and when. Any outcome can be reviewed, explained to a beneficiary, or reconstructed for a regulator.
Our definitions are being rewritten. Does that mean a rebuild?
No. Sprout.ai interprets the wording as written, so a revised definition is a configuration change rather than a development cycle. New conditions and new product wordings onboard on a small set of reference claims, tested against your handlers’ judgement before they go live.
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. Sprout.ai’s Guidewire Cloud Accelerator is available now.
