Case Study 3
From Monthly Guesswork to Daily Executive Visibility
A carrier's finance and executive team were making pricing and underwriting decisions based on loss ratio reports that were three to four weeks old by the time they arrived. The reports were assembled manually by a small analytics team that spent more time collecting and cleaning data than interpreting it.
The result was a leadership team that was perpetually flying slightly blind. Loss trends that should have triggered a pricing response in week two were not visible until week six. By then, the financial impact had compounded. The CFO described the situation plainly: the company was managing a real-time business with a monthly newspaper.
There was data available — plenty of it. The issue was that it lived in separate systems that had never been connected in a way that made it usable at the speed the business required.
Automated ETL pipelines were developed using Informatica to pull Incurred Loss and Earned Premium data from source systems into a central Enterprise Data Warehouse on a daily basis. The pipelines were built with validation logic to catch data quality issues before they reached reporting, eliminating the manual cleaning step that had consumed the analytics team's time.
Tableau dashboards were built on top of the warehouse, designed specifically for executive consumption — trend lines, ratio movements, variance flagging, and drill-down by line of business and geography. The dashboards refreshed overnight and were available to leadership each morning without any manual preparation.
Informatica | SQL | Tableau
The executive team gained daily visibility into loss ratio fluctuations for the first time. The analytics team's time shifted from data assembly to actual analysis. Within two quarters, the carrier made two targeted pricing adjustments in response to emerging loss trends that would previously have gone undetected until quarterly review.
The CFO signed off on the project close-out note with a single line: "We finally know what is happening while we can still do something about it."
The data existed. It just arrived too late to matter. Until it didn’t.
