How Financial Services Institutions Should Think About Unstructured Data — and Why It Matters for a Sound Enterprise AI Strategy

Being able to leverage unstructured data is a critical part of an effective data strategy for 2025 and beyond. To keep up with the competition and AI-accelerated pace of innovation, businesses must be able to mine the treasure trove of value buried in the mountains of unstructured data that comprise approximately 80% of all enterprise data — from call center logs, customer reviews, emails and claims reports to news, filings and transcripts. Even though it’s such a huge proportion of an enterprise’s data, many financial services organizations still don’t know how to effectively use it. 

The key? Having a solid data strategy with a platform that can support both structured and unstructured data. Without these, data leaders may have a hard time getting generative AI to run across an enterprise at scale to help optimize value. 

Businesses looking to take advantage of their unstructured data need to figure out how to accomplish three often challenging things: 

• Bring data in: What is the right paradigm for ingesting unstructured data? 

• Parse data: What does analyzing unstructured data look like? 

• Use the data once it’s transformed: How can data be accessible to different people across a business so they can find the right insights? 

Creating value for customers, one use case at a time

Being able to harness the above means data leaders can make strides toward optimizing tangible use cases that real customers can benefit from. Here are a few examples from across the financial sector where unstructured data can make an impact.

• Handling an insurance claim: The insurance claims process is intricate and essential to customer satisfaction. From the moment a claim is submitted — whether online, through a call center or via mobile app — it undergoes several key steps. The claim must be evaluated, routed to the appropriate department based on type and complexity, investigated for validity and ultimately resolved through settlement or payment. 

  This process often requires claims managers to review a wide range of data, including notes, contracts, call center logs and even multimedia such as videos and photos. Investigation may also involve fraud detection tools, on-site inspections and collaboration with external adjusters.

• Helping first-time home buyers: Buying a home is one of the most exciting milestones for many people, but it can also be a headache. The process requires a lot of documentation. Loan applications, income statements, tax returns and property appraisals all contain necessary information but can be difficult to process at scale. With AI-powered text-processing capabilities, agents and underwriters can more quickly and effectively parse documents, identify gaps or mistakes and expedite the home-buying experience for customers.

• Conducting quant research and investment analytics: Tuning into structured data such as pricing, estimates and environmental, social and governance (ESG) data is only the beginning of valuable quant research and investment analytics. For savvy asset managers, unlocking unstructured data with LLMs is the next frontier for generating alpha. Sifting through items such as corporate financial documents can be cumbersome, as can reading relevant news or understanding social media sentiment, which all can be helpful in understanding industry landscapes or shifting attitudes that impact markets. 

Without gen AI, using those unstructured data resources for market research requires advanced natural language processing skills and large time commitments. But with the right AI-powered tools, asset managers and quants can expedite summarization and equip asset managers to conduct more thorough — and unique — analyses, ultimately generating alpha (or at least faster insights that do). 

How Snowflake helps tap the power of unstructured data

Leaders in financial services are striving to map their AI and unstructured data strategy to the above opportunities and use cases, while maintaining security, scale and cost controls. Snowflake empowers those leaders with an AI stack that is easy to deploy, efficient to scale and trusted to maintain security. That stack is called Snowflake Cortex AI. 

Cortex AI encompasses “full-stack AI,” meaning it starts at the ingestion of data and runs through to the deployment of AI-driven applications. Cortex AI makes available functions to process unstructured data, create vector embeddings and run vector search, deploy foundational LLMs, build retrieval-augmented generation (RAG) architectures or chat with structured data in one unified architecture. 

Cortex AI allows builders to bring state-of-the-art models from firms such as Google, Anthropic, Meta, Mistral AI and more to their data, powered by scalable GPU infrastructure, rather than sending their data out to external models. This facilitates efficient deployment of applications and allows one governance/security architecture to remain in place as data progresses from ingestion to transformation to driving gen AI applications. 

This unified stack allows our customers to spend their time on driving AI ROI.

To learn more about how to leverage unstructured data, download the AI Blueprint for Financial Services or register now for Accelerate to see real-world demos.