Financial Services Companies Want to Scale GenAI – But Is Their Data Ready?

Reports of financial services institutions deploying generative AI (genAI) technologies are popping up everywhere. There’s good reason for that.

A recent Accenture analysis concluded that nearly every role in banking organizations can benefit from AI. Whether working in the front-, middle- or back-office, employees can use genAI to automate routine tasks and augment their personal capabilities.

The potential for improvement is significant. In fact, Accenture estimates that “73% of the time spent by US bank employees has a high potential to be impacted by generative AI—39% by automation and 34% by augmentation.”

And the business implications are clear. Surveyed C-level leaders believe genAI will help them increase their company’s market share, with 17% saying they anticipate a boost of 10% or more¹.

GenAI tools can improve speed and accuracy, reduce costs, and free workers from rote tasks to perform value-added jobs. They also help workers personalize customer experiences and better meet consumer needs, boosting satisfaction and loyalty and improving bank performance.

Yet deriving value from genAI technology is not a slam dunk. In most financial services firms, the data used to power genAI lacks the quality, accuracy and completeness to deliver optimum results. In other words, this data is not fit for business use.

How did this happen? For too many years, firms have under-invested in modernizing their data management practices. Those that tried to improve data cataloging, governance and integration typically relied on people, legacy tools and custom processes. Unfortunately, those approaches can’t scale to meet the needs of the cloud, digital and AI technologies that are fueling growth in financial services firms.

The result for many organizations is a risk gap – one that can be addressed only by modernizing data management. 

Leading financial services organizations recognize that data is no longer a byproduct of a transaction or interaction. Instead, data is a business asset or a product. Like any product, data needs to be curated, governed and cared for.

To ensure your data is ready for genAI, begin by identifying the desired business outcomes of your technology project. Understanding your definition of success from the start of a genAI project helps you determine where and how to invest in a wide variety of data management and data governance solutions. For example:

Data integration solutions to help access required data from any source, format, structure, volume, and latency to help build, train, and execute GenAI models and supporting systems at scale.

Data governance technologies helps organizations define and enforce data policies, improve data literacy across the business, and ensure proper access to sensitive data by those who are authorized to see it to avoid unwanted breaches or regulatory fines.

Data quality solutions help organizations identify errors, fix those errors, and ensure business has visibility into the current state of data quality to help business trust the data in their systems and applications.

Data catalog solutions allow users to understand where data came from, how it should be used, whether it is protected, and if it is accessible to specific systems or applications. By creating transparency, data lineage solutions help both technology experts and business users determine what happens to their data from creation, curation, and consumption

Master data management solutions helps organizations create a single source of the truth about their customers, accounts, counterparties, and services with insights into how each are related to each other for all analytical and operational systems to leverage for business use.

Data Marketplace solutions allows data consumers across the enterprise to get access to information about their data, access data quality scorecards, and access data at the source when they need it most.

These solutions are the foundations to help make data ready for AI use. It is important to consider solutions that are:

Seamlessly integrated with each other both at a process and metadata level. Adopting point solutions from different vendors can increase the cost of integrating and supporting them and  risk of point solutions not working with each other.  To avoid these situations, it is advantageous to consider a platform that includes these critical capabilities out of the box with prebuilt integrations between each that are designed to support each other and help scale your data management needs. 

As enthusiasm for genAI isn on the rise across the financial services industry, the demand for “fit-for-business-use” data grows exponentially. To deliver this data, you need to consider modernizing your data management and governance capabilities with modern solutions that are fully integrated, cloud native, and powered by AI and Machine Learning.

Informatica solutions allow financial services companies to manage and govern their data assets efficiently and cost-effectively. By offering our cloud-native Intelligent Data Management Cloud platform and our CLAIRE AI engine with copilot capabilities for intelligent automation, we help organizations ensure their data is ready for genAI.

Our powerful solutions support the data management productivity and scalability that financial services institutions demand. By ensuring that data is governed carefully, correctly and transparently, Informatica solutions enable companies to scale their AI, machine learning, and natural language processing investments to meet growing business needs.

The buzz around genAI is getting louder. Creating high-quality, well-governed, trustworthy data is the best way to ensure that genAI fuels your business growth and keeps customers engaged.  

To make sure your data is ready for genAI, visit us online.