Experian DataLabs

"Experian DataLabs provides a safe and secure environment to partner with our clients to enable breakthrough data experimentation and innovation."

Eric Haller, Executive V.P., Experian DataLabs

Partnering With Strategic Clients to Innovate

Experian DataLabs helps businesses solve strategic marketing and risk-management problems through an advanced data analysis process and research and development. Focused on innovating new data sources with an emphasis on financial services, telecommunications and healthcare, DataLabs helps deliver:

• Increased profitability
• Optimization of data assets
• Controlled financial risk
• Regulatory compliance

Experian DataLabs is staffed with a multidisciplinary group of data scientists with Ph.D.s and applied research practitioners with expertise in advanced analytics and machine learning, as well as other advanced statistical methods. Experts in applying cutting-edge data science to real-world business situations, DataLabs utilizes Experian’s vast data assets and is supported by Experian's global network and resources. Employing deep domain expertise and industry knowledge reduces client exposure during the development process.

DataLabs operates in a compliant, safe and secure environment for both internal and collaborative research to take place. Nearly a petabyte of data storage is provisioned by Experian in the United States to support research and development activities. The environment allows for multiple Big Data projects to occur simultaneously.

Big Data

Along with Big Data comes a unique set of business problems. From the obvious challenges of storing and accessing large data sets, to the bigger challenge of bringing together and interpreting disparate types of data. DataLabs effectively identifies and aligns solutions to address likely future enterprise needs.

Recent Experian DataLabs engagements

• Proprietary recommender system that considers frequency, value, accuracy, diversity and novelty of data. One implementation runs more than 28 quintillion calculations and leverages insight from more than 5 billion transactions. The approaches include collaborative filtering and merchant similarity indexing.
• Historical spend probability calculation and algorithms where individual profiles for consumers and small businesses are updated daily with new transactions; alerts are generated when events take place that are outside of expected behavior parameters. One implementation runs daily updates on more than 120 million individual profiles.
• Income estimation derived from anonymized automated clearinghouse deposits using advanced clustering techniques. The process can identify more than 60 distinct income deposit patterns, and the output has been linked to assess direct deposit account liquidity risk and cross-sell product sequencing.
• Other projects include evaluating the impact of multiple social media data sets on commercial credit risk, improving point proximity estimation using geolocation data and assessing consumer sensitivity to changes in product pricing.