Employers Holdings Inc. · Financial Services · Austin, TX · 16 weeks
Policy data quality score raised from 61 to 94
A mid-market insurance carrier spent $1.2M on an AI underwriting platform but couldn’t launch it because the policy data quality scored only 61 out of 100. We improved the data quality to 94 within 16 weeks, enabling the carrier to proceed with their $8.4M underwriting project.
Challenge
The carrier spent $1.2M on an AI underwriting platform that demanded a data quality score of 80 across 10 categories. Their policy data scored 61. The vendor’s fix would cost $2.8M. They needed a faster, more affordable solution.
Approach
We analyzed the vendor’s data quality rubric and found eight failing dimensions. We fixed six by building automated pipelines that standardized address formats, resolved duplicate policy IDs, filled missing coverage start dates, and normalized coverage code mappings. The remaining two issues involved legacy records and needed manual review workflows.
Outcome
By week 16, the policy data quality score hit 94. We launched the AI underwriting platform three weeks after delivery. The carrier projects $8.4 million in annual savings from improved underwriting efficiency. Our total cost was $340K, a fraction of the vendor’s $2.8 million estimate.
Vendor quotes didn’t even come close to the real problem. Here’s what we actually found.
The vendor was ready to scrap the whole policy system just to fix some data quality issues. That felt like using a sledgehammer to crack a nut. So, we rolled up our sleeves and got to work. We pinpointed eight specific data problems causing all the headaches. Then, we fixed each one with targeted solutions. No need to tear everything down.
We nailed 6 of the 8 key data fields without lifting a finger.
Here’s the deal: we had eight problem spots to tackle. Six of those? Totally fixable with automated data pipelines. So, we set up nightly jobs, hit the switch, and just like that, six areas were compliant in four weeks flat.
How We Fixed 17,200 Messy Records, No More Old-School Hassles
We jumped into the data and found around 14,000 old policies with coverage types that were a complete mess, super unclear. Plus, there were 3,200 records where the beneficiary info just didn’t make sense. To fix this, we created review queues and pulled in four analysts to handle the workload. Over eight weeks, they went through everything and cleaned it all up nicely.
Results
- $8.4M Annual underwriting value added
- 61 to 94 Data quality score
- $340K vs. $2.8M vendor quote
- 16 wks Delivery to go-live
We had $1.2 million stuck in a platform we couldn’t use because our data wasn’t ready. The vendor told us it would take another $2.8 million to fix it. Then Thinklytics came in and handled the whole thing for $340,000 in 16 weeks.