Driving Value from AI in Banking

Artificial intelligence (AI) continues to be one of the hottest topics in the financial industry. In fact, more than 40 different sessions touched on this topic at this year’s Money20/20 USA — from building trust in AI to AI-enabled everything. 

While everyone recognizes the opportunities and obstacles, many financial services providers are still just dabbling in AI. And, consumers remain skeptical about leveraging AI to help them manage their money.  A recent MX survey found that 43% of consumers would not trust AI to help them manage and track their finances. 

So, how can we balance the feelings of fear with the opportunities? How can those within the financial services industry introduce AI into their business processes in an impactful and meaningful way?

Here’s what leaders from MX, the state of Utah, Spring Labs, Moov.io, and Aliya had to say about AI, the keys for successful implementation, and top use cases in the financial services industry at last month’s Money Experience Summit. Watch the full panel discussion to learn more.

AI in Banking Takes Hold

AI is not new. IBM’s Deep Blue beat world chess champion and grandmaster Gary Kasparov back in 1997. And, the use cases for business have grown ever since. “[Businesses] have been using AI quietly for years,” says Sam Maule, Head of Business Development at Moov.io. This slow transition has allowed businesses to build and develop powerful AI solutions that paved the way for the AI that businesses are beginning to implement today.

Now, AI in banking is taking off at an accelerated pace. It is helping them to decrease costs and attract more customers as they simplify the customer experience with new self-serve tools. The banks that catch on will catch up and be able to challenge the competition.

How to Drive a Successful AI Implementation

With the proper leadership buy-in, businesses can begin to reap the rewards of newfound efficiency and start to do more with less. When it comes to determining what AI product will be right for a business, John Sun, CEO of Spring Labs, said “at the end of the day, a good AI product should not be defined by its AI-ness. It should be defined by its value to your organization.” 

AI solutions are not powerful simply because they are AI. The solutions that will have the most impact will simplify processes, improve the customer experience, and be easily adoptable. AI products should be measured by their ROI and present new levels of efficiency to delight customers.

AI Use Cases in the Financial Industry

Practical applications make AI more than simply an attractive concept. The panelists discussed some of the most important AI use cases they have seen within the financial services industry — and recommendations for where businesses can start: 

Delivering Customer Insights

AI offers a transformational functionality in predicting customer behavior more accurately and collecting customer insights. It helps financial institutions create a cycle of building products informed by customer insights, followed by generating more customer data to improve those products.

Automating Compliance Reporting

In light of the Consumer Financial Protection Bureau’s recent Section 1033 final rule, compliance is even more timely and important. Generative AI can be a powerful tool in helping to automate highly repetitive tasks, such as reporting requirements to meet compliance regulations.

Enhancing Transactional Data

While generative AI models have low accuracy for complex transactions, there are highly-involved model types that can accurately handle transactional data to drive personalized experiences and better support for the customer, while increasing the impact that financial service providers deliver.