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Differentiated, high-quality data at your fingertips

Our comprehensive data sets provide blended views of consumer and commercial data. Access our robust library of traditional and alternative data sources to start your data analytics and modeling strategies off on the right foot.

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Unlock deeper insights with predictive modeling solutions

No matter your needs, we’ve got the data analytics and modeling solutions that cover both consumer and commercial credit. Our predictive modeling tools and off-the-shelf solutions cover a wide range of customer and client profiles and can be implemented quickly and efficiently.

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Deploy explainable and custom models

Gain the edge you need to outperform in today’s market. Differentiate your business from competitors and create predictive analytics models specific to your portfolio. Optimize predictiveness with our ethical and explainable machine-learned models and deployable predictive modeling solutions.

How we can help

Leverage our data, prebuilt models and differentiated ability to deploy explainable and custom models to take your insights to the next level and optimize predictiveness.

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Credit and fraud attributes

Access comprehensive sets of credit attributes to help you make better decisions and prevent fraud across the customer lifecycle.

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Credit risk scores

Use scores to identify and mitigate risk. Forecast future customer behavior with high-performing generic and custom models.

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Data sources

Explore the numerous traditional and nontraditional credit data sources.

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Optimize decisioning with more granular segmentation that leads to more predictive insights.

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Trended data

Uncover patterns in customer behavior to manage portfolio risk and determine next best actions.

Frequently asked questions

Predictive analytics is a type of data analysis that uses algorithms and modeling to identify patterns in data and make predictions about future events. It involves using historical data to build models and strategies that can be used to predict future outcomes, such as customer behavior or market trends, and take appropriate actions.

A model in data analytics is a mathematical representation of a real-world process or system. It is a simplified version of the system that captures its essential features and behavior, which can be used to make predictions or decisions. Overall, models are an important tool in data analytics, as they allow organizations to represent complex systems and processes in a simplified way — and to use that representation to make predictions, optimize strategies and gain valuable insights.

Predictive analytics can be used in a variety of applications to help organizations make more informed decisions to better meet business goals. The use of predictive analytics can be applied to diverse industries, including marketing, lending, automotive and healthcare.


Let’s talk predictive analytics models

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