The use of NLP and GPTs in customer service represents a significant leap forward in banks ’ ability to understand and respond to customer queries .
Boosting operational efficiency The potential for AI to significantly boost productivity in the banking sector is widely recognised .
Prashant cites research from the IBM Institute of Business Value that predicts AI will add a 14 % increase to global GDP by 2030 , equivalent to a growth of US $ 15.7tn .
Ryan highlights the potential of AI to take over mundane tasks , freeing up data science teams to focus on more complex problems .
“ AI will take over more mundane tasks , like cleaning data ,” he says . “ Data science teams in banks , freed from monotony , will be able to focus on improving their algorithms .”
This shift in focus has the potential to accelerate innovation in the banking sector , as highly-skilled data scientists can dedicate more time to developing and refining sophisticated AI models that drive business value .
Viren emphasises that AI ’ s impact extends beyond customer-facing applications : “ It can boost real-time decision-making for leaders – for example by highlighting skill gaps within the organisation – alongside enabling better financial planning tools .”
This internal application of AI can help banks operate more efficiently , making better-informed decisions about resource allocation and strategic planning .
Modernising legacy systems Prashant points out that AI is playing a crucial role in modernising legacy systems in banks and insurance companies .
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