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While the hype has been real for about a year now, Gen AI has also started to deliver tangible results in the banking and finance sector. Here are some interesting examples from our customers’ recent projects – and I urge all Cobol coders to listen up carefully. 

Gen AI holds immense potential when it comes to productivity improvements, innovation and changed market dynamics, independently of the sector. It will surely bring about fundamental changes in the banking and finance industry too, we all agree on that one, but it’s not until we have tangible examples and concrete proof of concepts that we will know more precisely.

That’s exactly what’s happening right now.

Promising Gen AI pilots

Rising client expectations are driving end-to-end transformation in banking, making a compelling case for adopting Gen AI technologies. As a major partner to banking and financial services companies (we currently help 18 of the top 20 European financial institutions), we are involved in several promising pilots:

·         We conducted a Proof-Of-Value (POV) over six weeks for an investment and insurance company, where we evaluated Gen AI’s potential to tune its chatbot. As AI was introduced, the accuracy rate went up from 91 percent to 97 percent, with improvements in efficiency and fewer errors as other positive side effects.

·         A couple of months ago we launched a pilot for a European bank, where we saw a 35 percent productivity increase in coding using code companions – in our first trial. Efficiencies like this will have a huge impact on the industry (as on Cognizant as a company).

·         We did another interesting pilot over six weeks addressing Cobol code conversion at a UK based large insurance company. We all believed that there would be work for Cobol coders for a lifetime, didn’t we? we now see that Gen AI offers a faster, more flexible alternative to handling Cobol. Legacy structures will be transformed.

We also see that companies in general are increasingly interested in leveraging Gen AI for ESG purposes. In the recent Cognizant/Oxford Economics Deep Green report, 58 percent of respondents said that they have implemented AI/ML technologies to improve environmental sustainability.

A set of questions to get started

While Gen AI is no silver bullet, its preexisting models can be refined with business data to provide insights and create content with much less direction. The easy, “off-the-shelf-access” means that Gen AI democratizes innovation as everyone can utilize the same tools. What happens with competitiveness then?

“Two things will different you: data – your unique set of information that you feed into Gen AI – and the people within your company that can augment the results from Gen AI to improve the business”, says Duncan Roberts, Thought Leader and Futurist at Cognizant Research.

Where do you start then? One thing is for sure: without data, you’re going nowhere. Duncan Roberts urges companies to start by asking a few fundamental questions to get a framework for the project:

“What are we trying to achieve?​ What are the likely regulatory impacts?​ What are the skills we require?​ What data do we have and what do we need?​ What guardrails need to be in place?​ How will this transform our business and operating models?” By taking a holistic approach, you’re more likely to transform broadly and get ahead of competition.”

If you’re curious to learn more, please visit our pages for Banking and Generative AI Services.

Mats Johard

Country Manager, Cognizant Sweden

Mats Johard

In focus

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