Skip to main content

Machine Learning Predicts Outcomes at Primary Financial



Primary Financial Company manages an investment program for institutional investors to invest substantial funds in federally insured CDs. This break down to managing nearly 40,000 CDs and over $7 billion in assets while supporting relationships with 5,000 financial institutions and institutional investors. 

They wanted to improve sales targeting to predict both CD issuing companies' funding needs and institutions' desire to invest. As a massive company with a large portion of the industry's market share, PFC had a vast quantity of valuable data at their fingertips, but they didn't know how to leverage it to look into the future to predict consumer behavior. Having partnered with Fusion on several projects in the past, they turned to us once again to explore how advanced analytics and machine learning in financial services could provide data-driven, predictive outcomes. 


With a key objective in place, PFC and Fusion worked together to explore the following machine learning models:

  • Identify the best issuers for sales solicitation, including former, current, and prospective issuers
  • Provide rate guidance to investors and rate/term guidance for CD issuers
  • Target investors by likelihood of close

With a plan in place, our machine learning experts outlined the process, including data acquisition, transformation, model development, and predictive analytics. First, all private and public data sources were identified and acquired to gain insight into current and prospective customers. PFC and Fusion then worked together to determine meaningful and available factors. 

The next step was to transform the data so these factors would be consistent and accurate. With a solid foundation, Fusion developed machine learning models that would learn and identify patterns, then recognize those patterns when seen again to apply lessons to predict outcomes. We identified over 100 candidate "features" of data from both public and private data sources, then applied "practical analytics" which focuses on data that is applicable to the described use case. 

Having a clear understanding of the target accuracy of the predictive models was essential to the success of this project, but more important was client participation. By collaborating with our client, PFC learned to quickly identify and understand errant data and numbers to drive the value of our models even higher. Also, by defining the required utility of the models, PFC could realize business value without having to endlessly tune them. 


Improved insight into consumer behavior

The first phase of our machine learning was highly successful and equipped the PFC sales team with a qualified list of issuers to target based on previous profiles of customers. This allows them to maximize their finite sales and marketing budget to connect with prospective customers. At the same time, this collaboration provided a simple way for PFC's co-brokers to market the right product at the right time to the portfolio of investors. 

Identified Prospects to Target with Limited Marketing and Sales Resources
Built a List of Potential Investors and their Expected Likelihood to Buy a Specific Product
Gained Deeper Insight into Where CD Issuers Price their Instruments and the Rate at which Investors are Likely to Purchase

Ready To Talk?

Let us know how we can help you out, and one of our experts will be in touch right away.