How does a financial services firm improve sales targeting to predict its clients' desires to invest? Machine learning was the answer for PFC. Find out why.

Challenge

 

Long-time client Primary Financial Company (PFC) manages an investment program for institutional investors to invest substantial funds in federally insured CDs. The company:

  • Monitors, tracks, collects, and disburses principal and interest on nearly 40,000 CDs
  • Manages over $7 billion in assets
  • Supports relationships with 5,000 financial institutions and institutional investors

PFC wanted to improve sales targeting to predict CD issuers’ funding needs and institutions’ desires to invest. It partnered with Fusion on a pivotal initiative to explore how advanced analytics/machine learning could drive data-driven, predictive outcomes.

Solution

Organizations with the expertise to leverage machine learning will significantly widen the gap between themselves and competitors who aren’t able to move beyond traditional analytics tools. Fusion’s data science and application development teams work together to unlock business insights. We allow our clients’ data to tell the story without introducing bias to the underlying predictions. This hybrid approach allows us to apply agility in the process to rapidly operationalize insights into tools and platforms you use every day.

PFC understood the value proposition of machine learning and partnered with Fusion to explore the following machine learning models:

  • Identify 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

The process included four main steps: data acquisition, transformation, model development, and predictive analytics.

All relevant private and public data sources were identified and acquired to gain more information on current and prospective customers. PFC and Fusion then collaborated to determine meaningful and available factors.

Sources identified for PFC data acquisition: Private include insurance history, trade history, and third-party analytical data. Public include federal reserve bank (FRB), Federal Deposit Insurance Corporation (FDIC), Office of Thrift Supervision (OTS), National Credit Union Administration (NCUA), and Credit Union National Association (CUNA). These data points were identified as relevant and acquired by PFC to gain insights into current and prospective customers. They were integrated into the existing investment-platform issuer data to offer a higher distribution of information to pattern-match for the prospect.

Next, the data was transformed 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.

The first phase was highly successful, producing the following benefits, with more to come in the next phase.

  • Equipped PFC sales team with a qualified list of issuers to target based on previous profiles of customers. This enables efficient use of PFC’s finite sales and marketing budget
  • Provided a deeper understanding of where CD issuers need to price their instruments and the rate at which investors are likely to purchase. Understanding this spread allows PFC to potentially achieve larger-scale trading business
  • Provided a simple way for PFC’s co-brokers to market the right product at the right time to the portfolio of investors

Outcomes

Improved patient care

Right now, the system acts as a monitoring system that allows caregivers to consistently monitor their patients and respond in real time to things that could otherwise be catastrophic for a patient. By being able to monitor these simple vitals, caregivers have been able to mitigate negative patient outcomes and provide interventions in a timely manner.

Improved data security
Improved data security
Timely patient interventions
Timely patient interventions
Improved oversight and reporting
Improved oversight and reporting

Ready to talk?

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