Telecommunications, Cable & Utilities

Experian is excited to be chosen as one of the first data and analytics companies to enable access to SSA data for verifying identity against the Federal Agency’s records.

The pressure to innovate amid technological progress poses an opportunity for us all to rethink the work we do and the way we do it. Are you ready?

Experian Boost provides a unique opportunity to help dealers build loyalty while helping consumers.

The CFPB recently issued a NPRM to implement the FDCPA. If you aren’t ready for the new CFPB regulation, what are you waiting for? Read more!

Have you seen the latest TCPA class action lawsuit? Now more than ever, it’s crucial to build effective and cost-efficient contact strategies. But how?

To win with ML, the team and process are more important than the algorithm. Best practices before, during and after modeling to help you succeed.

Issues to evaluate during data sample selection and design for model development and an overview of traditional data sampling techniques.

Criminals constantly search and exploit weaknesses. In this digital age, protecting people fuels our commitment to identity protection and fraud prevention.

In banking, as in baseball, data and analytics are key to making informed, data-driven decisions for your team and your business.

At Experian, for machine learning, we use Extreme Gradient Boosting (XGBoost) implementation of Gradient Boosting Machines.

Machine learning's ability to consume vast amounts of data to uncover patterns and deliver results makes it well suited for the credit risk industry

While the marketplace struggles to manage the impact of fraud prevention, CIP routinely disrupts more than 10 percent of new customer acquisitions.

Experian recently interviewed Philip Bohi, Vice President for Compliance Education of AFSA, to learn more about his perspective on alternative data.

A summary of common resampling techniques that can be used to create a robust model development and validation sample.

Model validation is essential in evaluating and verifying a model’s performance during development before finalizing design and implementation.