Report

How to use GenAI to multiply customer insights from transaction data

Laying the foundations for success in GenAI banking

5-Minute read

Contributions from

Georgina Bulkeley

Director of EMEA Financial Services Solutions

Google Cloud

Vanessa Fernandes

Senior Advisor

Zup Innovation

Bryan Kirschner

VP of Strategy

DataStax

Co-written by

Jakub Piotrowski

VP of Product

Bud

Richard Berkley

Director of EMEA Financial Services Solutions

PA Consulting

In brief

Banks are shifting to an 'AI-first' strategy, leveraging GenAI and enriched transaction data to gain deep customer insights enhance operational agility.
Enriched data enables banks to offer hyper-personalized services, refine risk assessment, and tailor products, securing a stronger competitive position.
Successfully adopting GenAI requires data privacy, efficient deployment and strategic partnerships through a dedicated Centre of Excellence.

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Introduction

The ‘AI-first’ bank

The potential for generative AI (GenAI) to transform banking is huge. Banks are already harnessing GenAI to generate deeper insight for both humans and agentive models. This evolution is creating an ‘intelligent bank’ – one that adopts an ‘AI-first’ mindset similar to previous digital or mobile revolutions.

As banks begin to integrate GenAI into their strategic and operational processes, they unlock step changes in business agility, hyper-personalised customer service and automation at scale. Transaction data forms the deepest insight into both customers and markets, making it an essential asset in this transformation.

Two largest opportunity spaces for banks

Create semi-autonomous middle-office processes

Build more sophisticated customer engagement and assistance systems

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Scaling GenAI safely to multiply business value from transaction data

To fully unlock GenAI’s potential, banks must scale their applications safely and systematically:

Embrace strategic partnerships to extend the value chain and bring in external expertise.

Drive education and cultural change to support adoption.

Implement a rapid path-to-production that encompasses both technology and business engagement.

Build sustainable GenAI muscle through rapid, iterative development.

Establish robust governance to build trust.

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Conclusion

Create a transaction data and Insights Centre of Excellence

Banks should establish a transaction data and insights Centre of Excellence (CoE) to leverage GenAI and specialist tools for enriching customer transaction data and generating insights.
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This CoE should publish high-quality data and insight products and provide enabling services for bank users.
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The CoE should adopt an iterative approach, starting with simpler data enrichment techniques and gradually optimizing the use of GenAI.

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