Case Studies → Insurance
Cutting claims-fraud investigation time by 60%
A mid-market insurer partnered with Karsient to build a real-time fraud-scoring pipeline on top of a modernised claims data platform.
Client challenge
What was wrong with the legacy environment
Fraud signals scattered across claims, policy, and third-party data sources
Batch-only scoring meant fraud was often flagged after payout
Investigation teams relied on manual cross-referencing across systems
No feedback loop between investigator outcomes and the scoring model
Modernization approach
How Karsient approached the transformation
Karsient consolidated claims, policy, and external risk-signal data onto a governed Lakehouse, then built a real-time scoring pipeline that evaluates every claim at intake rather than after the fact.
Architecture
The modern target architecture
Streaming ingestion from claims and policy systems via CDC
Feature pipeline on Delta Lake feeding a real-time scoring endpoint
Unity Catalog governing access across claims, underwriting, and fraud teams
Investigator feedback loop written back to retrain the model on a schedule
Migration
Migration & re-engineering strategy
The legacy batch ETL was re-platformed incrementally, workload by workload, running in parallel with the existing nightly process until scoring parity was proven on historical claims.
Engineering improvements
What changed under the hood
Incremental, CDC-based ingestion replacing nightly batch loads
Delta Lake feature store shared across scoring and reporting
Automated data quality checks on every incoming claim
CI/CD pipeline for model retraining and safe rollout
Business impact
Measurable outcomes
Fraud triage time cut by 60%, from days to hours
False-positive rate reduced by 35%, freeing investigator capacity
Scoring now happens at claim intake instead of post-payout
A reusable feature store now powers two additional risk models
Technology
Technology used
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