Singapore-based Moneythor, the world-leading personalisation platform for banks, has announced the launch of its AI Suite, to enable banks to build deeper and profitable relationships with their customers.
The suite permits banks to leverage the full potential of their data to deliver experiences which resemble those of popular technology and media apps in terms of personalisation, proactivity and engagement – a challenge which many of the region’s banks are currently struggling to deliver in practice.
According to global research from Fintech Futures amongst banking and fintech decision-makers across the globe, only 23% of financial institutions would describe their acquisition approaches successful, nearly half (49%) admit that their current technology solutions are unable to provide engagement post onboarding, while a staggering 15% of newly acquired customers drop-off after the first 3 months.
Martin Frick, Moneythor’s CEO, explains that Moneythor’s new AI suite will squarely address the challenges facing banks to deliver deeper customer engagement.
“Specific, built-for-purpose AI is fundamental to the delivery of deep banking experiences; namely, hyper-personalisation, anticipation, and a proposition that extends beyond traditional financial offerings. And with respect to the latter, the opportunity is particularly compelling. Banks are, literally, at the intersection of people’s daily lives; whether around routine transactions or once-in-a-lifetime ones. Each provides an opportunity to connect with an additional service, or a complementary brand . . . . even beyond the finance space.” “Moneythor has been applying technologies such as big data, machine learning and AI to support the delivery of more personalised, predictive banking experiences for more than 13 years. Our new AI suite builds upon this ‘muscle memory’ with the addition of generative, conversational and agentic AI capabilities,” adds Martin
Specifically, Moneythor’s AI suite will enable banks to intuitively develop, test, deploy and adapt personalised customer content and recommendations, ‘on the fly’ by integrating with any (or multiple) Large Language Models (LLM), without the requirement to train each one separately. Resulting campaigns are not only deeply personalised, but self-adapting – thanks to the incorporation of Agentic AI – enabling them to adjust and respond to individual customer situations in real time.
