Automated Claims Triage and Fraud Flagging

Carriers and TPAs receive large, variable volumes of claims with inconsistent documentation (photos, notes, PDFs) and limited early indicators of fraud or complex exposures. Manual triage is slow and expensive, causing long cycle times, uneven workload across examiners, and missed early fraud signals that increase paid-loss leakage.

Build a mixed AI workflow that combines supervised scoring, NLP/CV, and rules to produce a prioritized, explainable claim queue and recommended actions; integrate this into the claims management system with clear human review gates and audit controls.

• Ingest: normalize policy data, intake forms, adjuster notes, photos, and 3rd-party data (repair estimates, vehicle history, telematics) into a unified profile.
• Models: use NLP to extract and classify unstructured text, computer vision to assess damage photos, and tabular ML to predict severity/risk; supplement with an LLM summarizer to produce concise claim briefs for examiners.
• Orchestration: calculate a composite priority and fraud-risk score; auto-populate structured fields, attach supporting evidence, and place claims into tiered queues (auto-approve, expedited review, full investigation).
• Human-in-the-loop & feedback: examiners validate recommendations, correct model outputs, and provide labels that feed continuous retraining; implement review thresholds so only cases above a risk cutoff trigger investigations.
• Governance & monitoring: maintain explainability artifacts, immutable audit trails of model decisions, regular bias and drift checks, KPIs for false positives/negatives, and a change-control process for threshold tuning.

Illustrative results: teams typically reduce manual triage volume by 30-50% and speed first-decision time by 20-40%, while early fraud detection can lower paid-loss leakage by ~5-15%. Overall operational cost per claim often falls by about 10-25%, with accuracy and reviewer productivity improving as models are retrained on operator feedback.