Using AI to Build Financial Security: 3 Questions Financial Institutions Should Ask Themselves

Over the past year, Commonwealth has hosted a series of Savings Innovation convenings in partnership with the Federal Reserve Bank of Boston (the Boston Fed) for banks, credit unions, and key regulators in the region. Each session focused on a different approach to address the troubling national statistic that one in four households do not have $400 readily available for emergencies. At our last event, held on June 14, 2018, we turned our attention to the use of big data and artificial intelligence (AI) to build customer-centric savings tools.

We invited a regional bank and a fintech company to share how they successfully integrated data and analytics and the impact it’s had on their operations and product offerings.

Jennifer Marino, Chief Marketing and Customer Officer at Rockland Trust, described their journey to using data and analytics to inform decision making throughout the bank - from reducing organizational costs and risk to improved customer lifetime value metrics and modeling. Chip Baker, Head of Decision Intelligence at Cinch Financial, unpacked their unique ability to create a user-centric decision advisor based on a holistic assessment of a user’s financial life. Both presentations helped to demystify some of the steps and barriers involved in leveraging customer insights for various purposes.

The presentations were followed by an interactive ideation session. The majority of the institutions in the room were in preliminary stages of assessing capacity and exploring possible use cases. Based on this discussion, we have identified three core issues that institutions will have to grapple with in order to incorporate AI and insights from customer data into product and service offerings:

1. Goals: What do you want to achieve for customers using data and AI technology?

Setting an intention for how data will be used is an important initial step. If your institution were able to mine and draw insights from customer balances and transactions, for example, what could that information be applied to? Would this capability be used for financial inclusion efforts and to improve customer financial security? Marketing and revenue generation? Both? Given the magnitude of the savings crisis, which impacts over 99 million people, we strongly encourage innovations focused on financial security.

In addition to the overall purpose, your institution might consider how the end-user (customer) will be impacted. For example, building out algorithms that recognize deposit and spending patterns can identify opportunities for a customer to save. How an institution acts on that information is up for discussion. In this scenario, savings could be automated (out of sight, out of mind), or you might use the same insights to engage the customer and nudge them to take a savings action. Similarly, a more holistic picture of a customer’s finances might reveal specific pain points and present opportunities to provide services like financial advising.

2. Operations: How will you access and analyze the data?

Core processors can serve as gatekeepers of data which is a challenge for financial institutions, like our convening attendees, who see data as an opportunity to gather actionable insights. Even when data is available, collecting, cleaning, sorting, and understanding the data requires a specific set of skills which many smaller financial institutions may not have. The question of whether to develop in-house capacity for analysis or outsource that skill permeated the group discussion. Your institution will have to assess its capacity and potentially acquire the right talent to fill any gaps.

3. Risks: How will you predict and manage risks associated with AI and data analysis?

Risks related to privacy and security, explainability of machine learning models, and unconsciously embedding bias into automation were among the group’s top concerns. On top of these risks, many participants expressed uncertainty about regulatory constraints related to AI technology. Conversations with regulators and risk and compliance departments early on are essential for any institution exploring these new capabilities.

 

The ability for your financial institution to innovate, expand your offerings, and improve consumers’ financial lives make these questions worth tackling. As Fintech products - from established strongholds and new players alike - raise the bar for customer experience, financial institutions of all sizes will have to keep up to stay competitive. AI, machine learning, and big data are powerful tools that can be utilized to help people build financial security. While the three questions we heard in our session represent challenges every institution will have to face, they also present tremendous opportunities for institutions to shift from bank-centric thinking to consumer-centricity.  

We look forward to continuing the discussion! Contact us at the link below, and look out for the next Savings Innovation convening in December 2018.

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