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DataRobot > Case Studies > Freddie Mac Advances Affordable Housing Goals and More than Doubles Analytics Productivity with AI
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Freddie Mac Advances Affordable Housing Goals and More than Doubles Analytics Productivity with AI

Technology Category
  • Analytics & Modeling - Machine Learning
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Finance & Insurance
Applicable Functions
  • Business Operation
Use Cases
  • Predictive Quality Analytics
  • Predictive Replenishment
Services
  • Data Science Services
The Challenge
Freddie Mac, a company chartered by Congress in 1970 to support the U.S. housing finance system, has been facing challenges in achieving meaningful predictions and key insights to inform business decisions. The company works with hundreds of thousands of customers and mines nearly four terabytes of data. However, they found that business intelligence and manual practices didn't scale effectively across this vast customer base and data volume. As market and economic conditions change, Freddie Mac must remain flexible and continuously deliver on its commitment to affordable, adequate housing. In a sea of unstructured and semistructured data, it’s challenging to achieve meaningful predictions and key insights to inform business decisions.
About The Customer
Freddie Mac is a company that makes home possible for millions of families and individuals by providing mortgage capital to lenders. Since its creation by Congress in 1970, Freddie Mac has made housing more accessible and affordable for homebuyers and renters in communities nationwide. The company is committed to building a better housing finance system for homebuyers, renters, lenders, investors, and taxpayers. Over the last 50 years, Freddie Mac has helped people realize their dream of owning a home more than 80 million times. The company has funded $11.6 trillion in mortgages and financed $6 million in rental units.
The Solution
To overcome these challenges, Freddie Mac turned to the DataRobot AI Platform to automate predictive analytics from data input through managing models in production. The analytics team creates models that span across the organization, bringing value to internal teams, lenders, and their end customers. AI helps the business make sense of the data. The platform extracts data elements from various text documents and images much more quickly and accurately than with the previous manual approach. The agency modernized its AI and ML infrastructure, shrinking the MLDev and deployment cycle to deliver meaningful value to the business rapidly. The DataRobot AI platform offers essential interpretability and explainability for stakeholders and compliance teams.
Operational Impact
  • Data scientists have proven concepts 2–10 times faster.
  • Saved 1,700+ hours per project.
  • Gained 2.7 times the productivity — speeding time to market and advancing affordable housing goals.
Quantitative Benefit
  • 2 to 10 times faster proof of concept.
  • 1700+ hours saved in model development time per analytics project.
  • A 2.7X productivity gain for a corresponding jump in time to market.

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