Experian’s Business Information Services (BIS) unit spent years building a valuable and unique database of corporate relationships to better serve customers. The unit created this database of corporate hierarchies using technology to do entity matching, and people who evaluated the matches and updated them based on research and human evaluation.
This process was time-consuming and limited the number of company hierarches the team could evaluate and match. It maintained a subset of corporate hierarchies out of a full universe of companies available to them due to the intense manual effort required. Plus, because of how much human involvement was needed, they were limited on how often they could refresh the hierarchies.
The IBM Data Science Elite team had a simple mission: apply AI to learn what Experian has done over the years building corporate hierarchies and then apply that to the full universe of companies that they traditionally couldn’t evaluate. The goal was to increase the number of corporate hierarches and increase the frequency of corporate hierarchy matching.
The results? AI and machine learning are now helping Experian solve a problem building and maintaining business families and corporate linkages with a potential 500 percent increase in coverage and 80 percent reduction in cost.
Our team began with a discovery workshop to understand the problem. This included digging into the data with an open discussion of the current process and what they would like it to be. This included a team of business experts from Experian, plus stakeholders who understood the value of a new solution and an IBM Data Scientist Elite who could help develop a new approach. After the workshop, we put together a plan which included leveraging machine learning. The plan was to train new machine learning models using Experian’s existing, validated hierarchies. Each hierarchy is a company with thousands of sub companies matched with years of Experian expertise, intellectual property and software