Future Branches Boston 2021

2021

The Westin Copley Place, Boston, MA

Renee Newman, Chief Strategy Officer at First Interstate Bank
First Interstate Bank Logo

Renee Newman


Chief Strategy Officer
First Interstate Bank

Check out the incredible speaker line-up to see who will be joining Renee.

Download The Latest Agenda

Day Two: July 24th 2020 Focus on What Matters Most: Your Customers & Their Needs in Branch

Friday, July 24th, 2020


8:30 AM Keynote: Thinking Like Tech Company Instead of Thinking Like Bank: Is it Actually Possible?

The financial services industry isn’t well known as being incredibly open to innovation. While the desire to innovate and keep up with customer demand is certainly there, it is harder to make the case for new technology or more streamlined experiences given the regulatory hoops that finance executives have to jump through to get things accomplished. Unfortunately, today’s customer doesn’t care whether or not something will be approved by your compliance team. In fact, they assume that all experiences they have should mirror the experience they have with Amazon- things should be easy & seamless but also leave them alone when they aren’t needed. During this keynote, Renee Newman from First Interstate Bank will talk us through how they’ve been trying to think more like a tech company to overcome resistance to innovation across the enterprise and create opportunities for improved customer experiences in their branches. 
• Creating a cultural shift in your organization towards acceptance of faster moving technology creation & implementation
• Putting together agile teams to test and pilot this technology that know they have the ability to ‘fail fast’ if need be
• Building adaptable, scalable tech that can easily change with changing consumer demand
• Bringing this holistic technological acceptance and agility to the branch when it comes to things like new software upgrades for employee technology, streamlined account opening with image capture or easier loan origination with AI and machine learning