# Claims fraud indicator review > Claims fraud indicator review: Indicators present and explained Checked: checked by the outcome. Produces referred or cleared claims. Source: https://www.sumarity.ai/library/claims-fraud-indicator-review/ Insurance # Claims fraud indicator review Reads claims, the indicators the team uses. Produces referred or cleared claims. Code works out every figure, the Judgement Engine answers the narrow questions, and whatever stays uncertain goes to a person. Checked by the outcomeScreening, audit and compliance Start from this workflowMore for insurance What it decides ## Narrow questions, each with a check behind it. Indicators present and explained Checked by the outcome. The truth arrives later: a payment, an appeal's result. Until the measured record earns more, people review a sample of what settles. | Reads | Claims, the indicators the team uses | Produces | Referred or cleared claims | Who signs | A person on your team, with every figure traced to its document and every call on record | Process | Screening, audit and compliance | Industry | Insurance See it run ## Every line, decided where it's safest. An example run. Each stage lights up as a line is decided there; pick a stage to see its lines. ReadsClaims, the indicators the team usesCodeReads, matches, ties out and works out every figure.JudgementIndicators present and explained. Settles when it clears the cutoff; people review a sample until the outcomes prove it.A personGets whatever stays uncertain, with the evidence. Their ruling tunes the next run.ProducesReferred or cleared claims Claims fraud indicator review · example run Example run | Ref | Item | Value | Decided by | CL-71 | Claim 3 days after inceptionIndicator present, explained by a new car · 0.90 | | Engine · settled | CL-72 | Invoice from a closed companySupplier deregistered | | To a person | CL-73 | Third claim this yearDifferent perils, all paid properly · 0.89 | | Engine · settled | CL-74 | Photos with old metadataTaken before the loss date | | To a person | CL-75 | No indicatorsProceed | | Code · clear | Settled 3 To a person 2 Settled wrong 0 Illustrative lines. Amber rows are the Judgement Engine's calls; each settles only above its cutoff and when its check agrees. What it could save ## Your volumes in. Your hours out. In our tests, between 24% and 42% of items still went to a person after tuning. Set your own share; a pilot measures it on your data. Items per run Runs per month Minutes per item by hand Cost per hour Share of items that still need a person: 30% By hand today80 ha month With Sumarity24 ha month, for the items people decide Saved56 hCHF 53,760 a year Related ## Start from what's closest. ### Screening, audit and compliance in other work Finance ### Supplier bank-detail change verification Whether a change request is genuine Cross-checkedFinance ### Expense claim audit Whether each claim fits policy; receipt matches the claim Proven by codeBanking ### Sanctions screening alert review True match or false positive Vetoed by code ### More for insurance ### First notice of loss triage Claim type, severity, fast track or adjuster Cross-checked ### Claim coverage check against policy wording Covered, excluded, limits Cross-checked ### Delegated authority bordereaux reconciliation Each risk and claim line against the binder Proven by code Questions ## What buyers ask What happens when Sumarity isn't sure? Nothing settles below the cutoff, or when the check disagrees. The item goes to a person in the inbox with what the engine saw, its best answer and the runner-up. The person's ruling is kept and tunes the next run. How are its judgements checked? The truth arrives later: a payment, an appeal's result. Until the measured record earns more, people review a sample of what settles. Can we change it to fit how we work? Yes. Start from this workflow and describe your differences in plain words. The design assistant revises it, the validator checks it, your expert reviews it on the canvas, and your admin publishes it. Does it read our files as they are? Yes: spreadsheets and CSV in any layout, PDFs, bank formats and e-mail attachments. Sumarity suggests how each column maps, proves the mapping on a sample and remembers it once a person confirms it. Where does our data go? Sumarity runs in Zurich. Your data, backups and logs are stored in Switzerland. How does it get better? Your team's rulings become an answer key. Better questions and cutoffs are proposed, tested on data they haven't seen, and published only when your admin approves. It improves itself, with permission. ## Start from this workflow. Run it on your data. A pilot runs alongside your own process for a few weeks, at our cost, and ends in a line-by-line comparison. Talk to usBack to the library