According to Forbes Magazine, the global AI market is expected to grow by more than $500 billion between now and 2030, according to various studies. IDC, a market research firm, predicted that the AI market will be worth over $500 billion by 2024.
While AI, machine learning, and related technologies are poised to transform the future of fintech, what do these tools mean for credit unions, and how can they be applied to unleash growth opportunities?
“AI presents an enormous opportunity for credit unions to transform the member experience on many levels,” said Dr. Kathy Snider, SVP Products Group Leader, Co-op Solutions. “By analyzing millions of data points about your members’ spending and transaction history, a sophisticated AI platform can help you uncover deep insights about your members and engage with them on a more personal level.”
Take card portfolios, for example. Credit unions can leverage the power of AI in varying degrees of complexity to gather and analyze member data points ranging from membership accounts, activation rates and number of transactions by category (PIN, signature and credit) to average ticket, revenue and expense. Benchmarking this type of data can help credit unions develop more informed penetration, activation and usage (PAU) strategies – and execute more successful marketing campaigns as a result.
At Co-op we are actively working towards bringing the benefits of AI and machine learning to the credit union industry. Here’s how credit unions can leverage AI and machine learning to gather and analyze member data to develop informed PAU strategies for their credit and debit card portfolios.
Penetration: Growing the Cardholder Base
Credit unions are known to have distinct advantages over larger banks when it comes to their credit and debit offerings, such as lower fees and interest rates. Yet members are constantly bombarded with credit card offers and bank solicitations. It can be easy for them simply use one of the more name-brand recognized card networks by default.
AI can help credit unions cut through the noise and optimize their card marketing strategy for frequency and relevancy. For example, machine learning can use spending and purchase behavior from existing members to identify the right time to engage them and determine the right offer. Let’s say your data reveals that certain members are more likely to travel during a certain month of the year; a machine learning algorithm could automatically segment out those members and send them a limited-time custom rewards or cashback incentive on travel-related purchases. Machine learning can also help identify account holders who may be good candidates for new card offers.
Activation: Simplified by Automation
Getting your members to activate and begin using their debit or credit cards can be challenging and most-often very labor-intensive. Tactics can range from instant issuance and automated alerts to sending out welcome kits, direct mailers or having dedicated representatives calling those members.
AI can be, and in many cases already is, used to automate these processes and reduce the strain on internal resources. For example, instead of having employees manually pull and review lists of inactive cardholders for activation campaigns, AI platforms can automatically identify those users whom have yet to activate their cards from within the system – and send out targeted e-mails accordingly. It could even set up a multi-step engagement strategy, without needing someone at your credit union to manually hit send every time.
Pre-programmed chatbots could also be implemented to automate activation services for members. Not only do these platforms eliminate the need to staff phone and chat lines, but members can get through the activation process faster.
Usage: Achieving Top-of-Wallet Success
Penetration and activation is half the battle but usage is the key driver of card portfolio growth. While rewards and loyalty programs are one of the more effective ways of driving cardholder usage, they work best when they are customized to the member. If members don’t feel the rewards they are receiving are relevant to them, they won’t feel incentivized to use them. In fact, research from Mintel suggests that only a third of banking members understand and actively use the rewards offers they are receiving.
This is where AI really provides deeper insight into your members. By designing rewards and other incentive programs based on your members’ behavior and transaction history, you are providing relevancy and value to those members in a way that feels more personalized. Some members may want cash back rewards while others might prefer airline miles – instead of presenting both options and making the member choose, machine learning can identify what offer your member is more likely to use based on his or her transaction history. Or, taking it a step further, you can use that information to provide your members with unexpected rewards. For example: rewarding a music-loving member with an exclusive cash back reward or discount offer on concert tickets. Personalization like this can go a long way towards achieving top-of-wall status and, more importantly, building loyalty with members.
Building Long-term Member Relationships
Optimizing the health of your credit or debit card portfolio is just one of the benefits that AI promises for credit unions. Ultimately, AI serves a tool that can inform our decision-making processes, helping us become more efficient and providing more personalized experiences for our members.
