Models & Scores

Alternative financial services data gives lenders access to powerful and predictive supplemental credit data that better detect risk and benefits consumers.

Given the option between offshore and onshore data science resources, how do you decide? Let’s discuss a few things to consider.

Experian is ushering a new age of consumer empowerment with Experian Boost, which eliminates the guesswork of what goes into a credit score.

An analytics environment can have enterprise-wide impact. Instant access to customer data, actionable analytics and intelligence tools drive the most value.

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

Any analytical environment is only as good as the data you put into it. Check these four key features when choosing the right one for your organization.

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

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.

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.

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.

According to our State of Alternative Credit Data research, more lenders are using alternative credit data to determine if a consumer is a good or bad risk

In an all-new report, Experian dives into “The State of Alternative Credit Data,” providing in-depth coverage on how alternative credit data is defined, consumer personas, and how this data complements traditional credit data files.

A thoughtful segmentation analysis contains two phases: generation of potential segments, and the evaluation of those segments.