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Wayak for Insurance

Process claims at machine speed.

From first notice of loss to fraud review, Wayak handles intake, scoring and verification so adjusters spend their time on judgment, not data entry.

Policy adminClaims DBDocumentsThird-party data
Illustrative Wayak workflow01 · Detect and route
Wayak
Sanitized demo workspace
Fraud pattern detectionClaims operations · illustrative Wayak workflow
Sanitized demo dataHuman-controlled
Wayak has this page in context
Ask Wayak what needs attention in fraud pattern detection...
Claims received
126
Payout held
$87.4k
Investigator review
11
Records requiring attentionUpdated now
CLM-28417Auto collisionDuplicate invoice · estimate mismatch 24%86 / 100Review
CLM-28409Property water lossVendor and address linkage detected72 / 100Review
What the operating loop is built to moveIllustrative workflow indicators—not customer-reported results.
Hours → minutesClaims cycle time
HigherFraud caught pre-payout
ConsistentAuditable decisions

Where Wayak creates leverage

Built around the real constraints in insurance.

The platform connects the fragmented evidence, focuses agents on the decision, and moves the right exception to the right person.

01

Straight-through intake

Capture and structure FNOL from any channel so adjusters skip the data entry.

02

Consistent, auditable decisions

Scoring and reserve estimates run the same way every time, with reasoning you can audit.

03

Catch fraud before payout

Every claim is scored against fraud indicators and the suspicious ones are routed for review.

See the operating layer

Every pain point becomes a visible, governed workflow.

Switch between three insurance examples. The product surface, data, finding, and controlled next action all change with the work.

Fraud pattern detection

Fraud is found after payout, if at all.

Value createdSuspicious claims flagged before money goes out.
Wayak
Sanitized demo workspace
Fraud pattern detectionClaims operations · illustrative Wayak workflow
Sanitized demo dataHuman-controlled
Wayak has this page in context
Ask Wayak what needs attention in fraud pattern detection...
Claims received
126
Payout held
$87.4k
Investigator review
11
Records requiring attentionUpdated now
CLM-28417Auto collisionDuplicate invoice · estimate mismatch 24%86 / 100Review
CLM-28409Property water lossVendor and address linkage detected72 / 100Review

One operating loop

From fragmented signal to accountable action.

Wayak keeps the source, agent judgment, human control, and output on one visible path.

01

Connect

Bring Policy admin, Claims DB, Documents into one working context.

02

Understand

a fraud-scoring agent that weighs claim, policy and external signals

03

Operate

a daily queue of high-risk claims for investigators

04

Control

Pause at the human decision, retain the evidence, then write the approved outcome back.

Grow from the first win

A focused starting point. A reusable operating system.

Begin with the workflow that hurts most, then reuse the context and controls across adjacent insurance work.

01
Claims intake

Capture and structure FNOL from any channel automatically.

02
Reserve estimation

Estimate reserves consistently from claim and policy data.

03
Fraud detection

Score claims against fraud indicators and flag the suspicious ones.

04
Document classification

Sort and extract from claim documents at scale.

05
Coverage verification

Verify coverage against the policy before payout.

Designed for real operations

Automation that shows its work.

Move faster without hiding the evidence, collapsing organizational boundaries, or handing judgment to a black box.

Evidence stays attached

Every finding keeps the source records and context that produced it.

People own judgment

Consequential decisions pause at an explicit human approval gate.

Access follows the workspace

Data, agents, and workflows stay inside the right organizational boundary.

Every run is visible

Teams can inspect the path from incoming signal to final writeback.

Bring us the insurance workflow everyone hates.

We’ll turn its scattered context, repeated judgment, and manual follow-up into a governed system your team can build on.