As a partner, Seven Peaks is glad to share content provided by Snowflake. In this article, we summarize some of the key trends related to data in financial services in 2025. You can also read the full article here!
Snowflake's "Data Trends 2025: Financial Services" report delves into the evolving landscape of data utilization within the financial sector, highlighting a strong pivot towards the practical application of AI and enhanced data strategies. The overarching theme is a transition from exploratory AI initiatives to the implementation of integrated, value-driven AI solutions.
Here's a breakdown of the key trends and insights from the report:
The Year of Realistic AI in Financial Services: The report explains that 2025 will be the year that financial services organizations move beyond the theoretical possibilities of AI and begin to implement grounded, practical applications. This shift involves a focus on integrating AI, machine learning, and data to boost productivity and operational efficiencies. Key to this trend is the need for organizations to demonstrate tangible value and ROI from their AI investments. The emphasis is on building solid data foundations to support the scaling of AI and generative AI across operations. This also includes navigating the complex compliance landscape, including AI governance and data protection.
Trends That Matter to Financial Services:
Enterprises are tapping more into unstructured data: The report highlights the significant volume of unstructured data within the financial services industry, including documents, emails, and call center recordings. There's a push to unlock the value of this data to improve customer understanding, services, and operational efficiency, often leveraging generative AI.
Data sharing and interoperability are becoming increasingly important: Financial institutions are recognizing the importance of leveraging both first-party and external data to enhance services and gain a competitive edge. This involves the ability to access, use, and enrich internal data with relevant external information and the use of tools that facilitate seamless collaboration and integration with partner solutions.
The hidden impact of data and AI on financial services operations: Beyond customer-facing applications, AI is playing a crucial role in improving internal operations. This includes streamlining data management, enhancing productivity through AI-driven automation, and utilizing techniques like semantic modeling to improve data utilization.
Use Case Spotlight: The report illustrates the application of these trends across different financial services subindustries:
Insurance: AI is used to streamline the complex claims process by efficiently extracting data from various documents, improving operational efficiency, and enhancing customer experience.
Asset Management: AI, particularly LLMs, are used to analyze unstructured data like financial documents and news, aiding in quant research and investment analytics.
Banking and Payments: AI is applied to expedite processes like home buying by quickly parsing documents and identifying gaps, improving efficiency for both agents and customers.
In conclusion, the Snowflake report emphasizes a transformative period for the financial services industry, driven by the strategic adoption of AI and data-centric approaches.