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Home Finance AI & RPA has Mixed Response for HSBC, Morgan Stanley & SG

AI & RPA has Mixed Response for HSBC, Morgan Stanley & SG

HSBC, Morgan Stanley, and Societe Generale are three very big names when it comes to the financial services industry. And while all three have recently experimented and implemented artificial intelligence (AI) into their business models, all three seem to be experiencing some problems.

Many firms across the globe, not just financial ones, have integrated AI into their production models in some way or another. The problem is that because AI itself is still a relatively new area that’s being explored there are still teething problems and new issues arising all the time.

Banking giant, HSBC has taken a dual-pronged approach to enterprising AI in its operations. Like most firms, it looked at its current issues and how AI can best solve them. However, it also went one step further using the technology as a starting point.

MORE – 10 Applications of Machine Learning in Finance

MORE – RPA – 10 Powerful Examples in Enterprise

“There are more vendors who want to sell you something in this space than you can count so sometimes [it’s a case of a] solution looking for a problem,” said Kirsty Roth, global head of operations at HSBC. “There were things that we saw that had worked in other places and we could see quite quickly how we could apply them.”

Similar challenges arose for those at Societe Generale Securities Services too when implementing robotic process automation (RPA) into their business model. “We’ve done quite a bit of work with RPA software recently and I have to say we’ve been a bit disillusioned with that experience,” said Matt Davey, head of business solutions at Societe Generale Securities Services. “When I talked to people internally, there was quite a lot of negative comment about RPA and the fact that it was really like a macro technology.”

READ MORE – Top 25 AI Software for the Banking Industry

The problem is, according to Davey, is that it’s very difficult to make changes to systems that have added RPA to it. “It’s a one-step move. It stops you from really making changes to your legacy platforms from there.,” he said. “The follow-on to RPA os we’re now looking at RPA with a combined AI component so that you have the AI component making the state of whatever the process is. We haven’t stopped it, we’re just changing the tool.”

Like others, Davey recognizes that the implementation of AI is needed for any thriving business to survive in this day and age. That’s the easy part. The hard part is knowing how to get that into production to solve these issues.

Source IT News

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KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
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