Beyond fraud: 5 ways machine learning makes your credit union smarter

For credit unions today, machine learning is proving invaluable in the fight against card fraud. Moreover, the technology’s potential to enhance your service proposition extends well beyond the realm of member security.

The sheer power of prediction delivered by machine learning has the ability to transform nearly every aspect of your business model. It’s hard to imagine a single area of a credit union’s operations that would not benefit from the ability to see accurately into the future. And your members will love it.

Although artificial intelligence and machine learning are expected to be deployed throughout credit unions, there are areas in which the technology is likely to be implemented first. Below are five immediate use cases for how machine learning can help drive credit union success:

#1 – Managing Risk More Intelligently

When layered over strategies like credit card pricing and credit line management, machine learning can help credit unions boost card portfolio performance significantly.

There is great potential for machine learning to more rapidly adjust pricing and credit lines according to emerging risks, which in turn will bring revenues into closer alignment with that risk. For credit unions, of course, it will be important to temper the recommendations of the technology with the people-over-profits philosophy.

#2 – Inspiring Member Engagement and Loyalty

Top-of-wallet strategies are another area of promise for machine learning because the technology allows credit unions to better anticipate member needs and preferences.  

Consumers demand highly personalized experiences, and they want service to be instantaneous. Predictive analytics delivered through machine learning can guide service models in a multitude of ways.

For example, new machine learning-based financial apps can alert consumers to bills they are overpaying – and to readily available, relevant discounts.

Imagine the surprise and delight your credit union could inspire by calling a member and saying, “We just found a way for you to save $50 every month on your cable bill, and we’ve already taken care of it for you.”

#3 – Taking Support Up a Level

While informed sales and marketing strategies are critical to building member relationships, so is providing world-class support. To that end, new voice-enabled machine learning solutions can augment and help expedite service.

But the real value machine learning brings to your support organization is its ability to analyze positive and negative outcomes from inquiries, and draw correlations between activities. With these insights, credit unions can better understand how members like to engage, which responses perform best and how to improve their performance over time.

#4 – Staying in Compliance

Keeping current on regulatory compliance requirements is a massive job and one that should not be left to the compliance officer alone. Every staff member must be aware of how his or her actions impact the member. That’s what examiners expect.

To help, new machine learning technology can be deployed to scan member interactions for trouble spots and provide real-time solutions that ensure compliance.

Call centers today are already analyzing speech patterns in search of terms, words and phrases that trigger ‘requirements’ for how the call should be handled. These systems can even populate the screens of representatives with scripts and checklists to follow – and then note the outcomes, learning from each incident.

#5 – Maintaining Your Competitive Edge

While it can be challenging for credit unions to change internal systems, practices and strategies to embrace machine learning technology, doing so is imperative to future success.

Your competitors are absolutely deploying machine learning. Startups are using the technology to power cognitive financial planning and money management apps. And large banks are rolling out chatbots driven by machine learning apps to get their customers better answers quicker. All of this is feeding a consumer desire for fast, personalized and cognitive service, and for exceptional experiences from the brands they favor.

Your credit union has the data. You have the member relationships, and you have the trust. Machine learning will only improve the experience you provide, helping you deepen relationships and earn new members.

Shazia Manus

Shazia Manus

Shazia Manus, formerly CEO of TMG, is CO-OP’s Chief Product and Strategy Officer. Shazia’s experience as a former credit union CEO gives her unique insight into the challenges ... Web: https://www.co-opfs.org Details

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