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Conversational AI in Finance: A Case Study with MSUFCU

Benjamin Maxim ()
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Benjamin Maxim: Michigan State University Federal Credit Union

A chapter in The Design of Human-Centered Artificial Intelligence for the Workplace, 2025, pp 371-385 from Springer

Abstract: Abstract Michigan State University Federal Credit Union (MSUFCU) first launched a member chatbot, Fran, in 2019 and learned many lessons. One of the most important lessons is that AI, while using technology, is not really a technology project but instead a service- or operations-oriented project enabled by technology. MSUFCU followed up with an internal chatbot, Gene, with a new partner to support their employees during the COVID-19 pandemic during an unprecedented volume of member contacts to the call center and live chat agents. After conducting a pilot through their innovation lab, MSUFCU calculated that Gene could automate over 2,000 employee-to-employee transactions each month, allowing for a considerable lift in productivity when hiring was not an option. This new partner’s platform allowed MSUFCU to manage the chatbot learning in a new way, allowing our employees to train the chatbot and improve its accuracy to above 95%, allowing them to be more responsive to their employees’ needs. These improvements gave MSUFCU the confidence to switch providers to Fran as well, which has led to Fran being responsible for over 33 FTEs each day, which in the post-pandemic talent squeeze has been extremely valuable. The chapter will also cover what goes into managing conversational AI operationally and strategically, as well as future opportunities and use cases MSUFCU explores for Fran, including voice and more secure banking transactions. The chapter will also discuss the use of Gene, a bot that uses They/Them pronouns and whose avatar has an androgynous look with colorful hair and ambiguous skin tone, to support MSUFCU’s diversity, equity, and inclusion initiatives and how the team involved MSUFCU’s employee resource group and DEI council to make intentional decisions in this space.

Date: 2025
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DOI: 10.1007/978-3-031-83512-4_21

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