The core-banking vendor won the investment and ringing endorsements from the trade group and several banks because its open system and cloud delivery could eventually challenge entrenched tech players.

In recent years, new machine learning algorithms and big data have reduced fraud losses to an extent — however, their impact has been relatively limited, in part because the industry has been reluctant to use them. But the use of such technology is soon likely to become far more widespread in the U.K., and across the EU.

Data scientists from NASA, Google and hundreds of other places are working for financial firms in their spare time. Is this a good idea?

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One of the concerns for no-cashier stores, beyond how well they work, is that they must also be placed in the middle of tech-friendly cities, where retail space comes at a high cost.

Robotic process automation is said to have a high return on investment, but adopting it on a large scale has proven challenging, especially at community banks.

In venturing into what's normally a province of large banks, nbkc in Kansas City, Mo., discovered innovative tax-management and other products that it could offer to its own customers or sell to other banks.

A machine learning-driven analysis can aid with risk, financial performance and fraud prevention, according to Anant Kale, founder and CEO of AppZen.

Banks and payment companies are already comfortable using artificial intelligence to guard against fraud. The tech could soon be just as welcome in creating payment products and matching them to potential customers.

The accounts — which eschew paper checks and overdraft protection — appeal beyond the low-income customers they were intended for; lenders are embracing artificial intelligence systems to analyze more data to determine creditworthiness.