David vs. Goliath: How NM financial institutions can leverage data to win against national banks
Yet, let’s face it. We live in a digital world where consumers demand online and mobile app options for commercial transactions – including banking. This was only accelerated in the past year with people sheltering at home instead of going into their neighborhood branch for banking transactions, a cup of coffee and a smile from the tellers and bankers they personally know.
It is unlikely that midsize and community banks will “out-tech” large banks and fin-techs on their own. The giant enterprises have teams of data engineers, data scientists and software developers in-house that work full time on customer mobile banking experiences, data analytics and more.
It does not make sense for local banks and credit unions to build departments of employees having this expertise, which is well beyond the skill set of most IT departments. Even if our local credit unions and banks wanted to hire these experts, they are scarce resources and really hard to find. Yet, data management and AI technologies typically are built for experts to use and often require you to write SQL code to use them.
So, how do our local banks and credit unions compete against big banks in a digital world?
Community financial institutions have an opportunity to thrive by redefining the local experience and digitally transforming how they operate. A side-by-side digital transformation model pairing technology and tools with access to data engineers and data scientists empowers mid-market financial institutions with advanced analytics and valuable business insights to improve customer relationships, strategically deliver new products and services through data-driven campaigns, and drive competitive advantage.
The side-by-side model gives smaller businesses access to the technical experts that they do not have in-house, which brings data analytics to community businesses that otherwise would not be able to use data mining and AI for insights to focus on critical business outcomes.
This levels the playing field against national banks, allowing our local banks to target, discover and offer the right services to the right people at the right time. It also helps them streamline their marketing campaigns to prevent filling our mailboxes with wasteful duplicate postcards offering products we already have.
Using the right data analytics, credit unions can leverage their local knowledge with personalized customer intelligence to regain competitive advantage. With artificial intelligence-powered data analytics, banks can learn more about their customers to grow their lifetime value, predict churn, understand which products to introduce to customers and when, based upon deep learning models that are informed by data specific to the financial institution.
As with many businesses, midsize and community banks have a plethora of data that is typically siloed across many systems throughout the organization. Banking core systems, lending systems, CRMs, website portals and third party data sources are all commonly used in day-to-day operations. Aggregating and integrating this data is a major challenge that can be difficult and time-consuming, if not nearly impossible, such as with transactional data. At the same time, mid-market banks struggle to achieve the valuable business insights that untapped data could provide to increase net deposits and improve operations.
In its 2021 Banking And Capital Markets Outlook: Strengthening Resilience, Accelerating Transformation report, Deloitte points out that “hyperpersonalized services that can factor in a customer’s financial well-being holistically should form the core of customer relationships. To achieve this goal, banks can integrate their disparate data architecture across lines of business and functions and combine it with AI-driven analysis to create a 360-degree view of customers.”
The data analytics solution that is right for mid-market financial institutions must include a platform built for non-technical business users that cleanses data for accuracy, ensures data governance across the organization, and employs AI and machine learning (ML) driven analytics to glean customer intelligence and insights from volumes of transactional data created in the business and updated daily.
With daily insights powered by a data platform, data models built from financial industry intelligence, and AI that enable a variety of analytics solutions for fast, easy access to credible data, bankers can find the answers in minutes to such questions as:
- Who are my current customers that have a loan and not a deposit account?
- Who has a mortgage or wealth account with one of my competitors?
- Which customers with a credit score above 700 are most likely to open a home equity line of credit (HELOC)?
- Which loans were modified from the previous day?
- Who are my current members with a HELOC that are utilizing less than 25% of their line of credit?
- How can we better identify loans at risk for default at the time of application?
- Which members are at risk for being victims of crypto-currency fraud?
Using data analytics, community banks and midsize banks have an effective and scalable way to leverage data for strategic business insights. Harnessing their data enables them to discover patterns, insights, trends and usage strategies which helps to strengthen their position in regional markets and compete with large national banks. With the right customer intelligence data model, they are enabled to deliver timely personalized messages to customers, make data-driven product recommendations, measure campaign ROI and grow net dollar retention.
Demand for mobile banking is only increasing. Yet, rather than kill northern Michigan businesses, investing in side-by-side digital transformation can provide opportunity for our treasured community credit unions and banks to deepen relationships with members and customers.
Katie Horvath is chief marketing officer at Aunalytics, Inc.