In Episode 3 of the third season of IIA’s Leading Analytics podcast, IIA CEO Jack Phillips talks with Sammi Li, Chief Analytics Officer of Community Bank at Flagstar. While the banking industry is undergoing a radical shift, driven by competition from fintechs and disruptive technologies, Flagstar is pivoting toward solving complex and critical business topics through hyper-personalized AI capabilities and other advanced analytics initiatives. Li is focused on understanding where Flagstar should apply data, tools, and technologies to deliver insights to their bankers and utilize data in the most useful way. Li advises aspiring analytics professionals to be curious, keep learning, and have a growth mindset.
Describe your title and role. Where does your function report within the organization?
My title is the Chief Analytics Officer (CAO) of Community Bank at Flagstar. I'm responsible for setting the overall analytics strategy and agenda for the Community Bank segment. As the CAO, my focus area includes shaping the data analytics organization, serving as a trusted adviser for senior leadership, bringing new perspectives, enhancing our data-driven culture, creating synergies across business and support functions, and developing BI and AI products to support our strategic initiatives. My organization has a data science team and BI data team. We cover everything around data reporting, business analytics, and innovation for Community Bank. And we also partner with functions across the bank on data architecture, data strategy, cloud strategy, marketing campaigns, sales incentive, and the data flows in and out of our product systems.
What are the most important application areas at Flagstar? Which business partners do you work with most?
It ranges from business as usual, which keeps our business running and operating, to advanced, predictive, and prescriptive business analytics. The banking industry is undergoing a radical shift, driven by competition from fintechs, new business model regulations, and disruptive technologies. We are pivoting towards solving more of the complex and critical business topics. First, we're focusing on hyper-personalization—how we can bring stronger and better digital and AI capabilities to deliver personalization to our customers through our bankers in the branches. We are building an integrated platform to drive the next best action for our bankers. There are other key areas we're working on with advanced analytics, which includes enhancing our product offering and pricing, customer retention, enhancing marketing and sales results, and also evaluating fintech partnerships.
What is your talent strategy for data and analytics personnel?
We acquire talent both internally and externally. Thanks to strong partnerships with schools and programs, I hired some top-notch data scientists to join my team, and that gave us a quick start. We also find a lot of passionate people with modern skill sets within the bank. We created a more flexible and collaborative model so that they can partner with data scientists, taking into account the most critical business problems. The career paths are not in any sense fixed within the organization. Our biggest thing is to see how people fit and where their interests lie. We open the door for them to evolve and learn new skill sets.
To what extent does Flagstar use advanced technologies such as AI and machine learning?
Within the personalization project, we apply AI/ML and, going forward, AutoML. For the personalization platform, we have applied clustering analysis, decisioning trees, and deep learning regressions to generate the next best conversation, or, the next best action, for our bankers.
What barriers to adoption do you see for advanced technologies?
The first challenge is, are we setting up something entirely new to the bankers? This could be threatening and drive low adoption. We went into branches to see what bankers are doing day-to-day—how they greet the customers, how they interact with customers, and what data and alerts can play a role in enhancing those experiences for bankers and customers. With that solid understanding, we know where we should apply data and tools and technologies to deliver insights to our bankers. The banker will see it as an enhancement rather than a disruption. The second area is the explosion of data we'll be able to deliver to our bankers about their customers. How can we best utilize that? Do we have the intellectual ability to use that information in a useful and correct way? This requires us to work hand-in-hand with many partners across the bank, including the sales leaders, branch leaders, and our sales enablement partners. With these two methods, we're hoping to introduce the data analytics products in a meaningful and helpful way to our bankers. I believe that will ensure high adoption as we roll out.
What advice would you give to aspiring analytics professionals?
- Be curious. Along with this, be humble.
- Keep learning. This is an exciting, interdisciplinary field. We have to learn statistics, computer science, business acumen, or for real case scenarios, new tools like Snowflake Data Cloud.
- Have a growth mindset. Always think about what you can learn, how you can enhance your skill set, and when you should benchmark your skill set to the market.
- Become a more helpful person. It's okay to set up a career goal like make more money or achieve a better title, but good things will follow naturally when you help others to create value.
For more insights from Sammi, listen to the full podcast.