Download W.UP’s white paper, and find out what banks can learn from other industries and competitors about implementing AI and ML-driven technologies, and how to unlock their power in sales and analytics

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Artificial intelligence in banking is all the buzz, and it’s not going anywhere.

AI is expected to contribute a staggering $15.7 trillion to the global economy in 2030, more than the current output of China and India together. In fact, it already plays a key role in boosting productivity, cutting costs and understanding customers in many sectors from retail to manufacturing. In banking, the race is definitely on: big techs and digital-only banks are entering the financial services landscape by the dozen and they’re all going after the big prize, sales. Is it any wonder that 40% of bankers plan to invest in embedded AI solutions over the next three years?

When it comes to the benefits and opportunities AI offers, the list is endless. About 74% of the organizations that implemented AI have increased sales of new products and services by at least 10%. One European bank, for example, started contacting customers proactively using predictive modelling and achieved 62% better sales results than with traditional campaigns. This white paper walks you through how to start the implementation process, what low-hanging fruits to reach for and how to maximise your return on investment.

Find out why banks need to:

Know how artificial intelligence and machine learning in banking can work wonders
Learn from other industries and competitors
Dig into AI use cases
Unlock the power of AI in analytics
Weigh ML options for their sales strategy
Reach for the low-hanging fruits
Team up with third-party providers

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