Alice Chambers |
The role of generative artificial intelligence in financial services is evolving and understanding its full potential is as intricate a baking a complex recipe, according Efi Pylarinou, founder of financial services content provider GrowFin.
Pylarinou hosted a panel at Money20/20 USA including thought leaders Uljan Sharka, CEO of iGenius; Christine Cavallo, senior vice president and head of strategy at Citizens; and Tyler Pichach, head of banking strategy for worldwide financial services at Microsoft, to discuss how technology providers are helping banks to ‘bake’ generative AI into their operations. Through the metaphor of baking, the session explored the nuances of crafting impactful AI-driven solutions for financial services by comparing the journey of AI adoption to creating the perfect cake the right ingredients, kitchen appliances and the best bakers.
Layering AI into banking
Pylarinou set the stage by comparing generative AI in banking to molecular gastronomy in cooking.
“In baking, we are moving toward combinations we’ve never seen before,” she said, likening generative AI to a crucial ingredient like baking powder. Just as baking powder drives chemical reactions in cooking, generative AI has the potential to transform banking with new solutions for productivity and innovation. But, as Pylarinou pointed out, “we’re going beyond general productivity applications in banking.”
The recipe for success starts with the basics: structured data from reports and social channels, just as a cake requires butter, flour, eggs and sugar. But assembling these ingredients requires a clear vision of how each component should integrate with the rest.
Precision and preparation for a successful AI recipe
“Success is in the details,” said Sharka, emphasising the need for careful process management when implementing AI. “It’s all about the process and understanding which ingredients to blend together.”
Sharka highlighted how banks need to approach AI with a structured mindset, breaking down the details for success and creating “ready-made mixes” for clients who prefer plug-and-play solutions.
“Not everyone bakes from scratch,” he noted. Some clients benefit from curated mixes – predefined, customisable AI models – while others choose to experiment with their own data “ingredients.” His company, iGenius, simplifies this process by providing large language model (LLM) blends and plugins to empower banks with tailored AI capabilities.
Structuring the kitchen: people and processes as key ingredients
Cavallo then likened the banking AI journey to a bustling kitchen, stressing the need for everyone in the organisation to understand their role from the “head chef” (AI strategist) to the “kitchen porter” (support staff).
“We’re thinking of the various chefs in the kitchen, making sure we consider risk, cybersecurity, talent and training,” she said.
Cavallo underscored the importance of a structured approach to governance, noting that a core governance framework is essential for ensuring quality and compliance across AI implementations. To make this shift, she advocated for continuous education and training.
“The human will always be in the loop,” she added, emphasising that while AI is powerful, humans remain essential for oversight, quality control and ethical decision-making.
Microsoft provides the essential kitchen appliances for generative AI
Pichach described Microsoft’s role as providing the “ovens and mixers” to help enterprises start their AI journeys. Microsoft’s solutions, from Microsoft Azure to Microsoft Cloud for Financial Services, offer banks the technology “recipes” to accelerate transformation while managing data, privacy and security. Pichach shared how Microsoft enabled one customer to support 45,000 contact centre agents with AI-driven knowledge management – a project that was initially expected to take two years but was completed in just four months due to employee enthusiasm.
In addition to the speed of adoption, Pichach highlighted the need for data governance and security.
“Security wins every single time,” he noted, pointing out that banks must prioritise protecting sensitive data as they deploy generative AI. Clients are also concerned with the pace of change and ensuring the underlying data is accurate. Pichach identified these areas as critical challenges in AI adoption, highlighting Microsoft’s commitment to a “security-first” approach to address these complexities.
A future-ready recipe for the AI-powered bank
While generative AI presents transformative opportunities, the panel speakers reinforced that successful AI integration requires more than just technology. It’s a combination of precise ingredients, process management and a well-equipped “kitchen.”
As generative AI adoption accelerates across industries, financial institutions are carefully assembling their recipes for success. With support from the Microsoft partner ecosystem, enterprises are creating custom AI-driven solutions, developing the skillsets and building the governance frameworks needed to meet future challenges.
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