AI / ML / Data Science — InsureSense (Insurance) Client / Industry InsureSense Insurance Timeline 14 weeks ongoing tuning Team Data scientist ML engineer backend dev claims SME Problem Claims processing was manual and slow; fraud detection relied on rules and missed complex patterns, increasing loss ratios. Solution Tkmetrix implemented an ML pipeline to triage claims, predict fraud likelihood, and automate low‑risk claim approvals with human review for flagged cases. Tech stack Key features delivered Automated claim triage with risk score and recommended action Fraud detection model with explainability (feature importance) Workflow integration to auto‑approve low‑risk claims and route high‑risk to investigators Outcomes 45% reduction in manual claim handling for low‑complexity claims Early fraud detection improved recovery rates by 18% in pilot Average claim processing time reduced by 40% Project process 1 Idea Data discovery with claims and fraud teams. 2 Scope refinement Define target metrics (precision/recall) and acceptable false positive rates. 3 Estimation Phased estimate: data prep → model → pilot → scale. 4 NDA & Agreement NDA + data sharing and anonymization clauses. 5 Development Data pipeline → model experiments → validation → deployment. 6 Launch Pilot on a subset of claims for 8 weeks.