All posts by Guest Contributor

When developing a risk model, validation is an essential step in evaluating and verifying a model’s predictive performance. There are two types of data samples that can be used to validate a model.

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

Digital credit offers and regulations. There’s no question today’s consumers have high expectations. As financial services companies wrestle with the laws and consumer demands, here are a few points to consider:

The key to data isn’t just accessing it. It’s interpreting it — and using it to make better decisions that benefit your business and your customers.

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

some synthetic identities are being used for purposes other than fraud. Here are 3 types of common synthetic identities and why they’re created

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

The business case for identity verification and risk assessment tools is most compelling when it includes a broad range of both direct and indirect factors. Here are 3 indirect measures we suggest you consider:

Market trends and insight from Q1 2018. The economy remains steady as we transition from 2017. Keep an eye on inflation and interest rates.

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

There is a delicate balance in delivering a digital experience that instills confidence while providing easy and convenient account access. When it comes to a frictionless, secure customer experience, consider these findings

Recent research shows Hispanics—especially Millennials who are entering their home-buying years—are particularly eager for homeownership.

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

A robust segmentation analysis contains two components: first is generation of potential segments, and the second is generation of potential segments.