This post was originally published on Fin Extra
What is the impact of hyper-personalisation in banking, and what are the use cases that will enhance the overall customer experience?
While front office modernisation will boost revenue, back office modernisation will decrease costs. How can organisations leverage AI to enable both of these drivers?
Banks need to consider end-to-end architecture when it comes to modernisation, so how can organisations approach application transformation in the most effective way possible?
The AI revolution in financial services is upon us. Traditional AI and generative AI have a plethora of use cases for financial institutions – from improved fraud prevention to streamlined operations – but one of the main opportunities it offers lies in the impact on customer experience that these technologies can have.
In an era where customer expectations are higher than ever before, AI technologies enable banks to offer tailored, hyper-personalised experiences to their customers, improving both customer satisfaction, lengthening the customer lifecycle, and increasing the overall revenue.
Yet reaping the front office benefits of hyper-personalisation is not possible without the back office. How can financial institutions decrease their reliance on legacy systems in order to leverage the opportunities of AI? Application modernisation is necessary to become
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