Case Study

How the world’s largest derivatives exchange reduced credit risk exposure and handled increasing volume through scalable technology

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A leading derivatives exchange handling more than 3 billion contracts annually

Capital markets

Business need addressed:
Consolidate customer entities across various disparate systems, reduce credit risk exposure, and implement scalable technology to handle increasing volume

Genpact solution:
Deploy a Data Matching Engine to cleanse customer data and establish links between databases, and to de-duplicate, match, and reconcile data

Business impact:
Standardization and unification of unique data entities

Business challenge

This client executes three billion contracts annually for a large, diverse customer base but could not get a single, consolidated view of these customers due to disparate exchanges and databases. Because there was no linkage between datasets, it was challenging to view the customer base as a whole, a process which involved tremendous manual effort and cost, resulting in a longer time to fulfill customer service requests. Additionally, the client’s credit risk exposure was higher due to duplication of customer entities.

As the organization entered a period of anticipated growth, the management team mandated scalable technology to meet growing volumes. Already strategically partnered with Genpact in the areas of application development and maintenance, BPM and mobility, and big data solutions, the organization was confident of Genpact’s thorough understanding of the market landscape and advantageous position in proposing relevant solutions.

The approach

At the beginning of its engagement, Genpact performed a thorough root cause analysis, using Lean and Six Sigma principles. This identified fuzzy matching and reconciliation as the client’s pain points. Much time and effort went into matching customer names duplicated between various datasets with no linkage. Genpact recognized that, in order to gain a consolidated view of the customer base, there needed to be comprehensive data cleansing, including data matching, de-duplication and reconciliation.

Since this is a common need in transaction-heavy industries such as capital markets, Genpact had already developed a Data Matching Engine to help enterprises deploy fuzzy matching, de-duplication and data reconciliation solutions and achieve operational and regulatory excellence. It was agreed that this methodology would be employed to address this client’s needs.

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The solution

Genpact’s Data Matching Engine uses powerful fuzzy algorithms to establish links between disparate datasets and to clean, de-duplicate, match and reconcile the multi-variant data sets. Typically, the solution caters to those datasets that encompass structured and unstructured data in the form of flat files, spreadsheets, financial transactional data dumps and corporate data including business entity names, employee information and product names.

The Data Matching Engine’s algorithms establish links between disparate datasets using Hadoop and distributed elastic search. This plug-and-play solution addresses different types of data fields and offers a user interface to configure matching rules, as shown in the following chart.

Business impact

Implementation of Genpact’s Data Matching Engine provided a vivid fuzzy matching solution to address the high cost of matching names, scalability to handle increasing volumes of data, and reduced credit risk exposure by eliminating duplicate data. Potentially, the organization expects to benefit from:

  • Reduction in operational effort by 37.5 hours per client’s customer annually
  • Cost savings of $1 million annually
  • Enhanced customer service levels with the ability to fulfill customer requests and meet desired SLAs
  • 10% sales effectiveness improvement, resulting from a consolidated 360-degree view of customers

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