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A Crawl, Walk, Run Approach to AI Adoption

Chris Cox, Chief Operating Officer

Apiture

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Trends tend to heat up and cool down quickly in financial services, with topics like blockchain, crypto, buy-now-pay-later, voice banking, and robo advisors dominating industry conversation, only to fade away. But generative AI promises to be more than a passing trend, likely leading to meaningful, lasting change. Given the early days of understanding and implementing AI, consider taking a “crawl, walk, run” approach when evaluating how your credit union can take advantage of this technology. 

 

Crawl: Establish a Foundation

As a first step, define the problems you want AI to address rather than thinking of it as a solution in and of itself. Having a strategy that spells out the use cases you will focus on is critical to maximize AI’s impact.

 

At present, AI has the propensity to “hallucinate,” making internal use cases, such as applications that drive employee efficiency, a good place to start until models are optimized to reduce the margin of error. For example, you could use AI-driven tools to:

 

  • Consolidate documentation sources to make it easier for employees to find and manage information

  • Automate meeting transcription and summarize next steps

  • Track project progress and optimize assignments based on employee workloads

 

To prepare for broader applications, you must also ensure your credit union has accurate, clean data since faulty data will cause poor outcomes no matter how robust an AI-based tool may be. You can choose to rely on in-house data professionals to build a data warehouse and cleanse, categorize, and normalize data so it is ready for use — or partners can do this on your behalf. Establishing data governance protocols to manage data access is also critical at this stage.

 

Walk: Enhance the Member Experience

Once you’ve demonstrated the effectiveness of AI in supporting internal teams, you can look to the technology to enhance engagement with your members. For example, AI can be used to help your member support representatives respond to questions more quickly.

 

Fraud detection and prevention is another use case that can enhance the member experience. A growing number of tools are using AI to analyze data from multiple sources to identify and flag potential fraud in real time — and these tools are likely to grow in sophistication. Identifying a potentially fraudulent transaction and asking a member to confirm its validity protects the member while building trust in your institution’s security measures.  

 

AI can also help you take personalization to the next level. Though credit unions are known for providing exceptional service and support to members, growing use of online and mobile channels means you must find new ways to deliver a tailored banking experience outside the branch environment. Look for AI-based tools that can help you segment your member base effectively so you can deliver highly targeted messages and offers. This added level of personalization will foster a deeper connection with your member base, even in a digital-first environment.

 

Run: Proactively Engage with Members

The next evolution of AI will involve implementing use cases where members interact directly with AI technology. Today, members engage with your institution by identifying the problem they want to solve — paying a bill, checking an account balance, or making a transfer, for example — and either going to a branch or using a digital channel to complete this activity.

 

As AI-based tools grow in sophistication, they will enable you to develop a more consultative, proactive relationship with your members — and will do so at scale. By understanding member behaviors and preferences, tools will suggest actions or products tailored to individual needs. For example, after identifying that a member hasn’t yet made his monthly mortgage payment, AI could ask if it should make the payment on his behalf.

 

Generative AI promises to have a dramatic impact on credit unions of all sizes. Taking a “crawl, walk, run” approach will enable you to implement appropriate AI-based tools with growing sophistication based on the acceptable margin for error. Look to partners you trust to help as you navigate this rapidly evolving industry.

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©2024, Finopotamus LLC. Copyrights for all articles belong to their respective authors; used by permission.

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