Financial Services

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

Dynamic pricing models for consumer financial products can be especially difficult for at least four reasons.

Not only are personal loans are increasing, but so is the share of those loans originated by FinTechs is also growing quickly across all generations.

You want to use big data, but how do you make your analytics truly actionable to stay ahead of the competition? Using an analytical sandbox is the answer.

With Hispanic Heritage Awareness Month underway and the topic of growing membership a constant priority, here are some tips from a credit union CEO.

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

Demand for data scientists is off the charts, but nationally there is a data science skills shortage. Many companies are filling this gap by outsourcing.

The MOBILE Act authorizes a standard for banks to scan and retain information from driver’s licenses and id cards as part of online onboarding process.

An Experian study revealed 86% of millennials believe that buying a house is a good financial investment - they just don't have the credit scores they need.

Even with the Fed’s gradual 2018 rate hikes, consumers are shopping. Usage of credit cards is on the rise in 2018 and only expected to grow.

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.

Rather than reinventing the wheel, companies can leverage existing services to build more complex solutions and launch faster with APIs.

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