Financial Services: Hidden Fraud Leakage

FINANCIAL SERVICES & INSURANCE CASE STUDY 01 HIDDEN FRAUD LEAKAGE IN CLAIMS OPERATIONS

Results at a Glance: • 27% reduction in fraudulent claim payouts within 5 months • 3x increase in fraud case detection rate • Claim investigation time reduced by ~35% • ~$4.2M annual leakage identified and controlled

About the Client: The client is a mid-sized commercial insurance provider operating across motor and property insurance lines in the United States.

What was actually going on: Critical datasets such as claims history, policy details, customer records, and adjuster activity were spread across multiple systems and never analyzed together. Fraud detection relied heavily on basic rule-based checks and manual reviews, which meant only the most obvious cases were flagged.

What DataVines Did: • Building a Unified Data Foundation: Consolidated data from claims systems, policy administration, and customer records into a centralized data layer. • Designing a Fraud-Centric Data Model: Restructured the data specifically around fraud detection use cases. • Layering Intelligence and Risk Detection: Introduced an intelligence layer using advanced analytics and anomaly detection techniques. • Enabling Actionable Visibility: Translated all of this into clear, decision-ready dashboards.

Challenges Faced: Dealing with inconsistent and incomplete data across legacy systems; claim classification varied across regions; shifting to a data-driven approach required careful alignment and trust-building.

Technology Used: Data Engineering, Fraud Analytics, ML & Anomaly Detection, Business Intelligence & Visualization Tech Stack: Snowflake, dbt, Python, Tableau, Fivetran

What Changed: The organization moved from a reactive model to a proactive system that flagged risks early. Decision-making became significantly more structured, with teams prioritizing claims based on data-driven risk signals.

Operational Impact & Efficiency: Investigation teams no longer had to review large volumes of low-risk claims. Claim processing cycles became faster, coordination between teams improved, and overall payouts were reduced.