Trends and Takeaways from Banking and Payments’ Biggest Event of the Year

This fall, thousands of leaders in the financial services industry gathered at the annual Money 20/20 conference to talk trends in payments, compliance, fraud reduction, treasury and transactions and more. Conversations centered on the theme of “Human x Machine,” and while AI was a focus, there were plenty of other insights around real-time data analytics, security considerations and customer strategies that are guiding the future of money. 

We caught up with some of Snowflake’s experts and partners to hear what they took away from their time there, what trending use cases they were noticing and what to look out for as banking and payments evolve for the rest of 2024 and beyond.

Data and AI architecture matter 

“Before focusing on AI/ML use cases such as hyper personalization and fraud prevention, it is important that the data and data architecture are organized and structured in a way which meets the requirements and standards of the local regulators around the world. Those requirements can be fulfilled by leveraging cloud infrastructure and services. It is important that the payment community understands the open file formats, open table formats and how the latest cloud technology leveraging GPUs will enable new data and data processes innovation going forward.” —Paul Chang, Head of Payment Networks, AWS

“Data warehouses are gaining a lot of momentum right now, and Snowflake is at the forefront of this trend. This is not surprising when you consider all the benefits, such as reducing complexity [and] costs and enabling zero-copy data access (ideal for centralizing data governance). Many folks were intrigued by how these benefits also translate into the customer experience; a best-in-breed tech stack that enables zero-copy data access to a customer engagement solution enables marketers to streamline marketing workflows and independently create segmentation or event-triggered experiences — all in a way that’s efficient, scalable and secure.” —Erin Bankaitis, Director, Industry Marketing, Braze

“The pace at which fintech startups either scale or fail is accelerating with advances in AI. It has become more crucial for them to consider data strategy at an early stage and how they work with other enterprise applications. Both VCs and businesses are pushing for more discipline in their investments, with a focus on a shorter path to growth and ROI. For both startups and enterprises, the data infrastructure and data sources they are considering deploying must have a clear route to value while being able to scale cost effectively.” —Sam Shapiro-Kline, Director of Product Marketing, TransUnion TruAudience Marketing Solutions

Ecosystems matter, too

“I see opportunity to drive more collaborative relationships across the ecosystem, including merchants. Currently so much collaboration is hampered because of a lack of shared understanding of needs and a lack of common data collaboration. Some of the techniques that are common in the media and marketing industry, such as identity resolution, could accelerate new use cases and new revenue streams amongst financial services companies.” —Prabhath Nanisetty, Industry Lead, Retail Data and Q-Commerce, Snowflake

“Data leaders in financial services must prioritize interoperability and data connectivity. As the financial ecosystem becomes increasingly interconnected, institutions need secure, scalable ways to share data across platforms and with third-party partners while maintaining high standards for privacy and compliance. Interoperability is essential to deliver a cohesive, cross-channel experience that aligns with customer expectations for instant, seamless transactions.” —Richard Winston, Global Financial Services Leader, Slalom

AI ROI is top of mind

“AI dominated the conversation at Money 20/20. It’s clear that many organizations have shifted away from proof of concept to real AI implementation this year. But AI can be expensive, resource intensive without a proven ROI, so discussions abound on which use cases were most relevant for financial services. At the highest level, there were two clear themes: internal AI use cases that drove efficiencies while lowering cost and commercial AI use cases that improved user experiences. Internally, banks are using AI to reduce the burden of data management, including data lineage and data quality controls, or drive efficiencies with business intelligence particularly in call centers. Commercially, we heard AI use cases around treasury services, fraud detection and risk analytics. What do these all have in common? Letting customers leverage natural language to gain insights and analytics on bank data.” —James McGeehan, Head of Banking and Payments, Snowflake

New opportunities with new technologies are worth exploring

“A surprising takeaway was the interest in the intersection of AI and digital currency (central bank digital currencies and stablecoins) as solutions for real-time, low-cost cross-border payments, highlighting a strong shift toward digital currencies that bridge traditional finance and modern digital platforms. Another key theme was the role of embedded finance, with brands increasingly exploring how to integrate financial services directly into their ecosystems to deliver frictionless, tailored experiences.

“One of the most impactful, yet underdiscussed, areas is the potential of autonomous finance, where systems not only automate payments but manage accounts and financial processes with minimal human intervention. Moving beyond mere automation, autonomous finance has the potential to transform how individuals and businesses interact with their finances, offering a highly personalized and dynamic approach that anticipates user needs. However, this shift requires new standards in cybersecurity, privacy and regulatory compliance to foster user trust and ensure regulatory alignment.” —Richard Winston

These experts also noticed certain use cases emerge in conversations. Below, they dive into three of the top trending ones to note.

Trending use case 1: Customer 360 and marketing analytics

“The financial services industry is eager to improve personalization in their messaging experiences. Ideally this personalization is maximized using the minimum amount of customer data to power these experiences. This priority has been a theme for many years, but many are falling short due to martech stack limitations, a topic that’s increasing in urgency and importance. As such, data leaders are coming to terms with how martech stack limitations not only impact the customer experience but also create risk from a data governance standpoint. As such, data leaders are prioritizing a best-in-breed approach that allows more seamless and secure data access, activation and distribution.” —Erin Bankaitis

“Financial services data leaders want to expand access to scaled data sets for marketing. They are increasingly thinking about how data sets are truly assets that can deliver value for multiple use cases across marketing, as well as teams outside of marketing. Leaders need an approach to connecting their data across marketing technologies like using unified identity resolution.” —Sam Shapiro-Kline

“Retailers and CGs are constantly wanting to learn more about their customers, not just for advertising purposes but for the pursuit of driving innovation and creating new products and services. Understanding more about how their customers operate within the financial services industry — like banking, investments or lending — can help them better understand the goals, drivers and barriers to different consumer groups, which could lead to new ideas around eliminating urban food deserts or expanding their brands to different price tiers.” —Prabhath Nanisetty

Trending use case 2: Treasury services

“With the amount of data sitting across multiple different systems and platforms, the treasury services use case is one that is prime for transformation with AI. The ability to embed AI into liquidity and capital analyses, including streamlining cash flow forecasting and reconciliation, as well as enable fraud detection, will separate the winners and laggers in the industry. AI will transform the experience of and democratize the power of data into the hands of corporate treasurers, allowing them to interrogate with natural language their ERP and other accounting systems.” —James McGeehan

Trending use case 3: Fraud detection 

“Another recurring theme was the urgent need for more robust, industry-wide sharing of fraud data and infrastructure among payment system participants. As AI and agentic AI technologies advance, they present dual opportunities and challenges: While they hold the promise of more efficient and secure payment enablement, bad actors are also leveraging AI in increasingly sophisticated ways to create harder-to-detect forms of transactional fraud. 

This evolution underscores the importance of collective defense; by pooling insights on fraudulent schemes and transactional anomalies across a broad network of participants, the industry can bolster its resilience against AI-driven threats. Such collaborative data sharing would create a more fortified ecosystem, where the collective intelligence of the many provides a stronger line of defense than any single institution or small group could achieve alone.” —Richard Winston

Interested in learning more about what’s on the horizon for financial services? Register for our 2025 Financial Services AI & Data Predictions webinar or get your copy of “AI Blueprint for Financial Services: How to Get Your Organization Enterprise AI-Ready.”