Turning data into a cohesive whole
The derivatives company has a huge, diverse customer base but didn’t have a single, consolidated view of its clients. Why? Because data resided in disparate exchanges and databases. And as datasets didn’t link up, the firm couldn’t easily see the customer base as a whole. Doing so required tremendous manual effort and cost—and that meant service requests took too long to fulfill. What’s more, with duplicated customer information, the company’s credit risk exposure was high.
We started by performing a thorough root-cause analysis, using Lean and Six Sigma principles. We quickly discovered the company’s pain points: fuzzy matching and poorly reconciled data. The enterprise was wasting a lot of time and effort matching customer names duplicated in various unconnected datasets. Getting a consolidated view of the customer base would take comprehensive data cleansing.
We weren’t surprised. This is a common problem in transaction-heavy industries such as capital markets. That’s why we developed Genpact’s Data Matching Engine—to help enterprises eliminate fuzzy matching, duplications, and poorly reconciled data. We wanted operational and regulatory excellence for the company and knew our solution would meet its needs.