Giving customer data an engine | Case study | Genpact
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Giving customer data an engine

How a large derivatives exchange reduced its credit risk

Who we worked with

A large derivatives exchange handling more than 3 billion contracts each year.

What the company needed

  • To consolidate fragmented customer information found in different systems
  • To reduce credit risk exposure
  • To put scalable technology to work to handle increasing volume

How we helped

We cleansed, matched, and reconciled customer data while connecting databases and removing duplication

What the company got

A crystal clear, full view of customer data with unique, standardized entities.

The firm was ready to grow and expected volumes to go up, so leadership gave the go-ahead to bring in scalable technology. Genpact and the exchange were already working together on applications, business process management, mobility, and big data solutions. The company knew that we understood its market and could propose relevant solutions, so it expanded our mandate.


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.

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A powerful data cleanser

Genpact’s Data Matching Engine uses powerful algorithms to link, match, and reconcile disparate datasets and clean and remove duplications. The solution works on both structured and unstructured data, including flat files, spreadsheets, and financial transactional data dumps. It also works on corporate data such as business names, employee information, and product names.


An enterprise with an unimpeded view

Genpact’s engine provided a low-cost, fuzzy-matching solution that is scalable and cuts credit risk exposure by eliminating duplicate data. The derivatives exchange expects to benefit from:

  • A 360-degree view of the customer created a 10% improvement in sales effectiveness
  • Reduced manual efforts
  • Cost savings of $1 million annually
  • Better customer service with the ability to fulfill service level agreements faster

This is yet another way that the company is getting great satisfaction from its partnership with Genpact.

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