Regional P&C Insurer · Insurance · Columbus, OH · 13 weeks
Claims data unified across 4 legacy systems
A regional property and casualty insurer struggled with claims data scattered across four legacy systems, lacking a common policy ID. This prevented them from assembling training data, stalling their AI underwriting project for 18 months. We consolidated the claims data into a unified foundation in 13 weeks, enabling the AI initiative to launch six weeks after.
Challenge
The insurer bought two smaller carriers over eight years but never merged their claims systems. Each system had its own policy numbers, loss codes, and date formats. The data science team spent 18 months trying to build a usable training dataset and failed twice.
Approach
We created a claims data layer that matched policy identifiers from four different systems by normalizing policy numbers, matching insured names, and aligning effective dates. We standardized loss category codes to ISO standards and consolidated claims history into one table. This table now serves as the single source of truth for actuarial analysis and AI models.
Outcome
We consolidated 1.2 million policy records from four separate systems into a single claims data foundation. Six weeks after delivery, we launched the AI underwriting model, which went live within the next quarter. This work increased loss ratio reporting accuracy from 71 to 97 of every 100 by aligning data across systems.
Clearing up those annoying policy ID mix-ups after acquisitions
The client had three different policy numbering systems across four platforms, totally disconnected. We fixed this by building a deterministic matching process. We combined insured name, address, effective date, and coverage type into a single key. That key hooked all their data together and made sure everything matched up perfectly across the board.
How We Dug Into ISO Loss Categories to Tidy Up Our Messy Data
Here’s what happened: every system was speaking a different language when it came to loss codes. Pulling a combined loss ratio felt like herding cats. So, we rolled up our sleeves and mapped all those codes to the ISO standards. Then, we built a crosswalk table to connect the dots. That simple fix let us slice and dice historical data across all systems without any headaches.
Results
- 1.2M Policy records unified across 4 systems
- $8.4M AI underwriting initiative unblocked
- 71 to 97 of 100 Loss ratio reporting accuracy
- 6 weeks Time from delivery to AI initiative launch
Our data science team worked for 18 months on creating a training dataset for the AI underwriting model and ran into dead ends twice. Thinklytics pulled all our claims data together in 13 weeks, and we rolled out the model six weeks later. It’s up and running now.