Finovate Fall Recap
By: Tyler Brown
SEPTEMBER 17, 2024
Finovate Fall last week signaled topics that were top of mind in fintech and which companies had the ambition and traction to present. Artificial intelligence (AI) was wildly popular at the expo and in the general session — we counted eight general-session presentations focused squarely on AI and generative AI. Unlike in recent years, Banking-as-a-Service and embedded finance were more often framed as risks than opportunities, and conversations about crypto were sparse and brief.
Some things we noticed from the Finovate expo, demos, and sessions:
CCG Catalyst will be at Money 20/20! Stay tuned for the recap.
Data Strategy in the Age of AI
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SEPTEMBER 19, 2024
By: Tyler Brown
Artificial Intelligence
Using data effectively is a core business priority in banking, suggests a sample of C-level bankers CCG Catalyst surveyed in 2023. When asked to choose their top three priorities for the next 5 years, 50% of respondents picked “leveraging data analytics to improve operations and opportunities.” With legacy infrastructure, years of technology debt, and divisions within the organization itself, that’s much easier said than done. Without a single source of truth, financial institutions (FIs) face redundant, missing, or conflicting data. That data fragmentation can hamper an institution’s day-to-day operations and long-term strategy.
Fragmented data is a sales and customer retention problem. Different pieces of data, like demographics, product ownership, spending behavior, or financial goals, are isolated from one another, preventing a holistic view. As a piece in the ABA Banking Journal notes, data may fail to line up with what another department sees, siloes don’t have immediate access to certain data sets that may help them do their jobs, and data provided by different vendors may not be compatible in the first place, forcing manual work and suspect results.
In the age of artificial intelligence (AI), efficiency is the tip of the iceberg for data management. AI models need consistent, correct, and up-to-date data to function well; fragmented data must first be sourced, formatted, and merged in ways that make it useful. Tens or hundreds of applications within different business units for different use cases will make it hard to do that. To effectively use AI models in the long run across the bank’s functions, data must be centralized and synced in real time.
To reach this end, FIs need a data strategy — establishing what data they will collect, how they will manage it, and how they will use it — driven by business strategy and supported by the right technology. That takes technical chops within the FI and a vendor-assessment process that acknowledges the role that data will play in the FI’s long-term success. It isn’t just about internal data sources controlled by the FI. External data sets are also a crucial source of business intelligence and analysis.
As we’ve written, there are a few crucial things to know about the data an FI holds before it implements a data strategy. Knowing what data you have, where it comes from, and its quality are early steps:
Modern infrastructure is also fundamental, and better data governance is another step — sharpening the people, procedures, systems, and metrics that help an organization control and have visibility into its data.
An FI’s data is a vital competitive advantage — particularly what it knows about its customers. But data is of value only when it’s usable, and the volume of data required to train and feed AI-driven systems make that imperative even more important.