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Energize Engagement with Artificial Intelligence in Banking

Imagine you’re tasked with finding your most engaged account holders based on their use of your products and their spending. To do this, you must build an audience which includes lookalike account holders who have the potential to increase their engagement with your institution. Remember to track account holders who are less engaged to reduce the risk of them eventually attriting. And make sure you’re meeting all of the latest banking rules and regulations around privacy and use of artificial intelligence (AI) in banking. Could you accomplish this manually? Traditionally, it might take a team of data analysts and data scientists a year or more to create a customized AI model for one such segment. 

Artificial intelligence in banking can now help financial institutions identify highly engaged account holders, offer them products most likely to add revenue, and deepen their relationships. In an industry first, Alkami’s Engagement AI Predictive Model creates a full spectrum of account holder engagement. The Engagement Model helps financial institutions grow revenue from engaged accounts while retaining at-risk accounts. This targeting can impact more than revenue growth, such as fostering brand ambassadors who are less price sensitive. 

How Alkami’s Engagement AI Predictive Model Works

Designed from the ground up for financial institutions, Alkami’s Engagement AI Predictive Model seamlessly integrates into financial institutions’ technology stack. This means financial institutions can incorporate the model into their current systems without significant disruption, allowing for a smooth transition to more data-driven, predictive marketing strategies. The aim is to enhance existing capabilities, not replace them, making it easier for financial institutions to use artificial intelligence in banking for better engagement without the need for extensive technical overhauls.

So how does it work? Alkami’s AI Predictive Modeling considers metadata tags as it searches for account holders demonstrating behaviors significant to the outcome it is trained to predict.  The Engagement AI Predictive Model uses the output of a model that predicts attrition. The model is trained by observing the behavior of account holders who have closed accounts. 

From there, all account holders are scored based on their likelihood of attrition. Those who score high on the model are added to an “Attrition Risk Positive” audience. Those who score low for attrition are highly engaged account holders. The Alkami model creates audiences for the entire spectrum of engagement so financial institutions can target each level of engagement with appropriate messaging.

The Four AI-Derived Engagement Groups Explained

One of the best ways to grow a financial institution is to focus on making loyal account holders more profitable. With Alkami’s Engagement AI Predictive Model, banks and credit unions can focus on growing the business of account holders who already have a firm relationship with your institution. These account holders are highly receptive to the financial institution’s offerings and are most likely to expand their banking relationship.

As part of the Engagement AI Predictive Model process, each account holder is placed into one of four groups:

  1. Highly engaged account holders – most likely to adopt your products and services. These account holders use multiple products, maintain high balances and show lots of money movement in and out of accounts, including receiving ACH payments and making regular outgoing payments.
  2. Moderately engaged account holders. Experiment with cross-sell marketing to this group. Account holders who are moderately engaged can be targeted with relevant add-on products and services.
  3. Somewhat disengaged account holders. Account holders who are somewhat disengaged can be targeted for retention, even before they fall into the highly disengaged group.
  4. Highly disengaged account holders – most at risk of attrition. This group can be targeted with re-engagement campaigns. Deploying re-engagement marketing that significantly cuts account churn rates is a big benefit to financial institutions. The more accounts retained, the more acquisition expense saved.
Engagement Simplified with Artificial Intelligence in Banking

Now, Alkami’s bank and credit union clients can drive engagement marketing campaigns with powerful predictive models that provide audiences of account holders most likely to seek one of the institution’s products, such as an auto loan, certificate of deposit, checking account and savings account. 

When it comes to tailoring marketing campaigns, Alkami’s Engagement AI Predictive Model analyzes behaviors and preferences indicated through metadata tags. This enables the creation of highly personalized messages that resonate with the specific needs of each group. For instance, highly engaged account holders might receive messages about premium services or loyalty programs, while disengaged account holders could be targeted with incentives to rekindle their interest in the institution’s offerings.

By precisely identifying and engaging account holders based on their engagement levels, financial institutions can achieve improved retention rates, increased revenue from targeted cross-sell opportunities, and a reduction in account churn.

 

Learn how Capital Credit Union is using Alkami’s Predictive AI to maximize retention and growth.

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