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.
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.
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.
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.
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