Financial Solutions

It’s the holiday season, and we’re prepared for holiday fraud. Our team monitors FraudNet to identify anomalies as millions of transactions occur this month

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.

There's a lot of talk about alternative credit data today, but not all of it is factual. Dispel the myths and learn what the truth about alternative data.

Children are attractive victims since fraud that uses their personal identifying information can go for years before being detected.

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

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.

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.

Alternative credit data - think financial services data and rental data - adds supplemental insight into consumers when paired with traditional credit data.

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.