AI for Insurance Companies | Claims & Fraud AI

AI that reads loss notices, medical records, repair estimates, police reports, and adjuster notes and extracts structured data automatically -- eliminating 20-40 minutes of manual data entry per claim across your entire incoming volume. Document type classification runs first: the system identifies whether an incoming document is a loss notice, a medical bill, a repair estimate, or a police report before applying the appropriate extraction model. Vision models (Azure Document Intelligence, AWS Textract, or GPT-4o with vision) handle scanned and photographed documents where layout analysis is required. Fine-tuned extraction pipelines (LayoutLMv3 or custom transformer models) handle digital PDFs with variable format structure. Field-level confidence scores returned with each extracted value: high-confidence extractions populate your claims system directly; low-confidence extractions route to a human review queue showing the source text alongside the extracted value so the reviewer can confirm or correct in seconds rather than re-reading the entire document. Output is structured JSON mapped to your claims system's field schema -- ClaimCenter, Guidewire, Duck Creek, or a custom system -- pushed via API with error handling and retry logic. Document receipt acknowledgement generated automatically after extraction completes. Extraction accuracy reported per document type and field as a quality metric reviewed monthly, with model retraining triggered when accuracy drops below configured thresholds.

Automated first notice of loss processing that extracts information from phone transcripts (via speech-to-text transcription and NLP extraction), web form submissions, emailed loss notices, and mobile FNOL apps -- normalising all intake channels into a single structured FNOL record regardless of how the policyholder reported the loss. Coverage validation at intake: the reported loss date is checked against the policy effective and expiration dates, the reported peril is checked against the covered causes of loss on the policy, and the reported location is checked against the insured property address -- flagging coverage questions before the claim opens rather than discovering them at adjudication. Claims system field population: policyholder name, contact information, policy number, loss date, loss location, loss description, and reported damage are written to your claims system (Guidewire ClaimCenter, Duck Creek, Majesco, or custom) via API. Complexity triage classification: the FNOL data is scored against a complexity model (simple/moderate/complex) based on loss amount indicators, loss type, coverage complexity, and litigation indicators -- routing simple claims to automated adjudication queues and complex claims to experienced adjusters. Initial acknowledgement letter generated and sent to the policyholder within minutes of FNOL intake, including the assigned claim number, adjuster name and contact, and the acknowledgement deadline compliance documentation required by state regulations. Average time from FNOL receipt to claimed system entry reduced from hours (manual processing) to under 5 minutes.

Classification models trained on your historical claims data -- approved payments, denied fraud claims, and SIU referrals -- where the ground truth is your own outcomes rather than industry benchmarks that may not reflect your specific book of business. Feature engineering for insurance fraud detection covers multiple signal categories: claimant history signals (prior claims frequency and severity, prior SIU flags, policy age relative to claim date), provider signals (medical provider billing patterns, repair shop concentration, attorney involvement patterns), geographic signals (claim rate by ZIP code versus premium written, loss clustering), timing signals (claim filed on a Monday for a Friday night loss, claim filed immediately after policy inception, claim filed just before policy cancellation), and document signals (inconsistencies between submitted documents, image manipulation indicators in photos). At intake, the fraud scoring model runs in real time and returns a fraud probability score and the top contributing features. High-score claims route to your SIU queue with the contributing evidence pre-surfaced -- the investigator sees the signal immediately rather than discovering it during manual review. Score threshold tunable against your SIU capacity: too low a threshold floods SIU with false positives and delays legitimate claims; the calibration is agreed with your operations team before go-live. Model performance monitored monthly against actual investigation outcomes; retrained quarterly or when fraud pattern drift is detected in the feature distributions.

Underwriting risk models trained on your historical policy data and associated loss outcomes -- the policies you wrote, what happened, and what you paid. The model learns the relationship between applicant and risk characteristics and actual loss frequency and severity within your specific book of business, producing predictions calibrated to your exposure rather than industry average tables. Feature inputs at quote: structured data (property characteristics, construction type, occupancy, prior claims history, credit score where permitted, geocoded location risk scores from third-party data providers like CoreLogic or Verisk), and unstructured data (inspection notes, prior loss descriptions, correspondence text parsed via NLP to extract risk signals that do not fit structured fields). Risk score output: a numerical score, a risk tier classification (preferred/standard/non-standard/decline), and the top 5 contributing factors displayed to the underwriter in plain language -- not a black box number. Underwriters use the score as a starting point for complex accounts rather than building the risk picture from scratch for every submission. For personal lines with high submission volume, the risk score triggers automated binding for preferred-tier risks meeting all other eligibility criteria, reducing underwriter time per policy by 70-80% on the submissions that don't need judgment. Score calibration validated against hold-out loss data to confirm predictive accuracy before deployment. Recalibration scheduled annually as new loss outcomes accumulate.

NLP models that systematically scan claim notes, adjuster reports, police reports, and correspondence across your open and closed claim portfolio for third-party liability signals that indicate subrogation recovery potential. Signal categories the model identifies: references to at-fault drivers or parties in vehicle claims, product defects or manufacturer liability in liability claims, property owner negligence in premises liability claims, employer liability in workers' compensation claims, and contractor negligence in property damage claims. The model extracts the relevant text snippets and the named parties, ranks claims by estimated recovery potential (based on claimed amount and signal strength), and routes the top-ranked flagged claims to the subrogation team with the evidence pre-surfaced. For closed claims: retrospective scanning of the claims database identifies recovery opportunities that adjusters closed without pursuing -- industry benchmarks suggest 3-8% of closed claims contain recoverable subrogation that was missed during handling. For open claims: real-time flagging during active claim handling so subrogation investigation runs concurrently with claims resolution rather than being initiated after closure when the trail is colder. Statute of limitations tracking per jurisdiction and claim type -- the model flags subrogation candidates approaching the filing deadline to prevent recovery opportunity expiration. Recovery pipeline dashboard showing identified opportunities by estimated value, investigation status, and expected recovery timeline.

AI-powered monitoring of claims handling against jurisdiction-specific compliance requirements across every open claim, updated as regulatory deadlines approach -- replacing the manual tracking that relies on adjuster memory and spreadsheet reminders. Acknowledgement deadlines: most US states require written acknowledgement within 10-15 business days of FNOL; the system tracks receipt date against jurisdiction requirements and flags any claim approaching the deadline without a recorded acknowledgement. Payment timeframes: prompt payment laws vary by state (10 days to 45 days after proof of loss in most jurisdictions); the system tracks the proof of loss date, calculates the deadline per state, and escalates claims approaching the payment deadline to the supervisor queue. Denial letter compliance: NLP analysis of denial letter drafts checks for required elements (the specific policy provisions relied upon, the specific facts of the claim that trigger those provisions, the right to appeal, the contact information for the state insurance department) before the letter is sent -- flagging missing elements for the adjuster to correct. Documentation requirements per line of business: workers' compensation 30-day investigation requirement, personal auto statement deadlines, property claim inspection requirements -- each tracked per claim and flagged when the required action is not recorded. Compliance audit trail generated automatically from the claims handling record: for regulatory examinations, the audit trail shows every required action, whether it was completed, and when -- compressible to a regulator-ready report without manual compilation.