Artificial Intelligence (AI) is a growing topic of conversation amid just about every industry, financial services included. The promise of cutting-edge tools that can mimic the kind of work a human could provide in a mere instant is clearly something many industries will want to consider. There are many solutions currently available that promise just that, but how do you find the right one for your credit union? 

With every new technology to enter the hype-cycle, there will always be vendors looking to capitalize on it. How do you select a solution that will last, versus the ones just looking to cash in while the buzz is at its peak? For a small to medium-sized credit union, this choice can be even more crucial. How do you make sure you’re making the right choice the first time, and avoiding the potential monetary and time cost of a wrong decision?

Here’s a look at what value you could find, what can go wrong with a poorly implemented AI, and what to look for in a vendor that will bring real value to your credit union. 

The avenues of AI

Tech-savvy credit unions and other financial institutions have been hard at work finding ways to incorporate AI solutions into their workflows. The potential efficiency gains and improvements to both the member and employee experience are hard to ignore, and with numerous use cases popping up, it’s no wonder that AI is on everyone’s minds. 

One of the most common use cases is through a conversational chatbot. These guided self-service tools automatically answer user questions and guide them through your digital properties, such as your website, online banking, and online applications. These can relieve an overloaded call center by making it easier for members to solve their own problems.

AI is also quite often used in fraud detection, using algorithms to detect bad actors and unusual behavior on accounts so they can be shut down before any damage occurs.

Data analysis is another popular use case for AI in credit unions: Algorithms that read into recent trends and offer insights. These can be used to create predictive models for member behavior, helping to guide smarter decision-making in the future. 

These are just a few examples of what can be done with new AI developments, and the potential rewards are certainly promising. However, there is still much to consider before selecting your AI vendor, especially with how much could go wrong should you go with one that isn’t right for your business. 

Potential pitfalls

Generative AI, the type of AI model that includes ChatGPT and Meta AI, is a relatively new technology. Particularly for these kinds of business-facing, practical applications, there’s still a lot of potential for what can be done with these tools. Unfortunately, this also means there’s still a lot of potential problems that can arise – especially for credit unions where the impact to consumers is high.

As our relationship with AI grows and develops, old problems may fade out and new problems may arise, but for now here are some common points of concern often seen in modern AI models:

• Hallucinations: Generative AI isn’t always accurate in the information that it provides. While the responses it provides often sound natural and correct, the AI isn’t good at fact-checking itself to ensure accuracy. When an AI model presents false information as true, these are often referred to as ‘hallucinations’, and they can cause serious issues for those looking to make any use out of AI tools. 
• Security and legal concerns: AI has drawn some controversial attention in regards to potential security or legal flaws, especially when considering the compliance needs of the financial industry. Potentially leaked member information, biased training data, and hallucinations spreading misinformation are all issues that a credit union won’t want to risk, no matter the potential efficiency gains. 
• Incompatibility: Most AI solutions of this caliber were simply not built with financial institutions in mind. Not only is it generally not made with the security needs of the industry, but is also not built with the proper implementations into the kinds of software that credit unions typically use. This will mean a lot of retrofitting and extra work will be needed to get them operational, which racks up the costs and time commitment even higher, further increasing the risk of failure. 

Where to find success

To ensure that you gain all the benefits of AI for financial institutions without falling into any of these potential failings, look for a vendor that puts responsibility at the forefront. Responsible AI prioritizes safety and functionality over how innovative or powerful it may be, ensuring results and preventing the kinds of fatal mistakes that can cost an organization dearly. 

Here are three of the main qualities that you should be looking out for in a potential AI vendor to ensure that they’re doing Responsible AI that will truly help your business:

• Safety: This AI provider should be able to provide the kind of security that all other financial products offer. Look out for security certifications and encryption levels that meet the strict compliance needs of the financial industry.
• Turnkey: Your AI provider should be making a product specifically for your industry. This AI should be able to fit within your pre-existing workflows and not require extra effort to incorporate into your systems. 
• Proven You should be able to see actual, real results instantly from this solution. There should be established metrics of success from real, verifiable customers that you can rely on to show there’s real value to be gained from the AI. 

With these guiding principles in mind, your first foray into the world of AI should be fruitful and avoid all the common areas of danger that so many other solutions have fallen into.