Artificial Intelligence in Banking: Is Your AI Model Actionable?

Today, companies across all industries are leveraging cutting-edge advances in artificial intelligence (AI) in banking such as AI predictive modeling, machine learning, natural language processing, and other forms of AI. This powerful technology enables them to provide relevant, instant recommendations and personalized experiences to consumers.

Artificial intelligence in banking is reshaping the industry by boosting efficiency, enhancing account holder engagement, and promising significant cost savings. According to McKinsey’s 2023 global banking report, AI has the potential to save the financial services industry up to $300 billion annually.

Financial institutions leveraging data analytics in banking to understand account holders’ product adoption, utilization data, financial spending patterns, and lifestyle insights to identify cross-sell opportunities is now a standard practice for the most digitally mature banks and credit unions.This approach is similar to the personalized recommendations consumers receive from various services they use daily. Account holders not only accept that their data will be collected, analyzed, and used to enhance the quality of their service, they expect it. Alkami’s recent commissioned research in partnership with The Center for Generational Kinetics shows that 44% of digital banking Americans wish their financial provider offered a more personalized digital banking experience, including 56% of younger millennials (ages 28-35) compared to 28% of baby boomers (59-65).

Data Analytics in Banking: The Most Powerful Tool in a Marketer’s Toolbox

Access to meaningful and actionable data insights allows marketers to understand account holders’ financial patterns and lifestyles, enabling the delivery of personalized and relevant engagements and experiences.

Large banks and credit unions with significant resources stay at the forefront of innovation with sophisticated data analytics in banking, similar to those used by Netflix and Amazon. In banking, AI models can use data from product adoption, utilization, and transactions to enhance account holder retention, mitigate risk, and develop needs-based cross-selling programs. 

Community financial institutions that have traditionally lagged in adopting new technology can no longer leave it to the big players. Now is the time to get started. Shouldn’t every marketer have AI in their toolbox?

Future-Proof Your Financial Institution

Data-driven technologies have the incredible power to transform our lives and influence everyday decisions. Financial institutions that leverage data analytics and artificial intelligence in banking to predict future behaviors and personalize account holder experiences will achieve more intelligent, user-centric, and timely engagement and communication. This approach builds trust and ultimately leads to a more profitable institution. 

Effective AI predictive models delve deep into the financial institution’s full spectrum of account holder data, analyzing key moments in their lives, the financial transactions they process, their use of products, and their behavioral patterns. These insights can be used to forecast future behaviors and identify trends that enhance the relevance of marketing messages. The uniqueness of transaction data—such as spotting account holders making micro-deposits to a robo-advisor or an increase in car insurance payments—combined with product data, is the predictive modeling approach that financial institutions need now. 

How to Mobilize Artificial Intelligence in Banking

For optimal results, an AI predictive modeling solution should be implemented alongside a comprehensive suite of account holder insights. This combination provides context to predictive model outcomes and identifies engagement opportunities that can drive changes in account holder behavior.

Predictive model results alone have limited effectiveness without additional insights to make the results actionable. For instance: 

➜ How can you deepen your relationship and win back the business of account holders likely to churn if you don’t understand their current behaviors with your products or their activities with competing institutions?

➜ How can you identify account holders likely to open a HELOC without understanding their lifestyles to deliver relevant engagements? This may result in wasted marketing dollars, as your message could get lost in the noise of surrounding advertisements.

➜ How do you target the right audience with account offerings like a money market? It’s essential to understand not only their deposit balances with you, but also their transfers to external investment accounts and their overall borrowing activity. Communicating that your goal is their financial well-being is crucial for building trust in your account relationships. 

Artificial Intelligence in Banking Requires a Human Touch

By adding a human touch and personalization to account holder interactions, combined with insights from predictive modeling, financial institutions can build strong connections, foster trust, and ultimately drive growth. To enhance this trust, it’s essential to develop a “digital brand personality” and provide personalized experiences during key moments in the digital journey. 

Additionally, bank and credit union leaders should challenge themselves to become a “data-informed digital banker,” utilizing the digital banking platform for both sales and service.

For a deeper dive into how AI and human intelligence work better together, check out our blog post, Boost ROI with Artificial Intelligence in Banking.

Using a wealth of insights and AI modeling, financial institutions can create optimal experiences tailored to the right account holders through the right channels. This approach not only drives growth and improves account holder satisfaction but also enhances account holders’ financial well-being, increases their engagement with the institution, and boosts overall profit.

Is your financial institution ready to take advantage of AI predictive models?