Something was thrilling about playing connect the dots as a child. Making an image come to life from a hundred seemingly unrelated points could keep you occupied for hours. The process of following one dot to the next and uncovering how each one came together to form something bigger was almost like magic.
Fast forward a few decades, and that thrill is at risk of disappearing. In the financial services industry in particular, you’ll find a lot of bankers struggling to connect the dots. Banks sit on a wealth of unstructured data housed within documents, emails, polices and procedures, and so on. When brought together, all of this data paints a larger picture of how a unique financial institution operates.
The problem is, nobody is bringing all of this information together. Traditionally, it lives in static files, requiring humans to manually locate, read, and retain it. But such efforts are a massive time suck. A recent study from Hapax found that bankers spend an average of 24 hours per week (nearly three full workdays) on manual tasks like retrieving information, updating content, and navigating compliance.
To put it simply, most bankers are stuck staring at the dots, and they don’t know how to draw the line to connect them.
Enter: Generative AI
Generative AI holds the key to fundamentally changing how banks interact with their data. The challenge here lies in identifying the right tools for the banking use case. Not all solutions are created equal; finance is a highly regulated industry, and every institution operates within its own unique context. Policies, procedures, best practices, and more will vary from bank to bank depending on where they’re located and the communities they serve.
As such, relying on general-purpose tools like OpenAI isn’t an ideal path forward. Banks and credit unions need guidance that’s tailored to their specific industry, location, and institution. They need to know with 100% certainty that the tools they rely on are giving them the most relevant and accurate information.
Banks should therefore look for solutions that meet two critical bits of criteria. First, they need purpose-built tools. Solutions that pull information from anywhere on the internet aren’t going to cut it, but those built specifically for banking will come to the table with a wealth of industry-specific knowledge. These solutions will have critical information like state regulations and a fundamental understanding of how banks operate baked into their design.
Second, the right tools will enable institutions to upload their own data. This will ensure that every output generated will be based not only on broader banking expertise but also on an in-depth familiarity with how a specific institution operates. When generative AI has access to your bank’s internal policies and procedures, it can provide hyper-personalized guidance that is always relevant, accurate, and compliant. This is where purpose-built tools truly stand out. While most tools allow for data uploads, purpose-built solutions remove key limitations. They support greater volumes of data and offer enhanced security, giving institutions the confidence to fully leverage their information.
The Intelligence Core
The dots should be starting to come together now. We’ve established that they’re nearly impossible to connect manually, but the right generative AI tools can facilitate the process. The best solutions will operate as an “intelligence core,” or a single source of truth that understands how all the data points a bank owns relate to one another.
The intelligence core provides the context necessary for bankers to do their jobs as effectively as possible. It connects the dots automatically and builds a knowledge graph of relationships that make each contextual component both actionable and accessible. The right tool will take dense documents and break them down into usable data parts that can inform people, processes, and workflows in a way that is simultaneously:
- Aligned with how your bank operates
- Safe and secure
- In line with regulatory requirements
The data that banks are sitting on can be instantly actionable; they just don’t realize it, or don’t have the means to understand it. This data provides background on how and why certain things are done (and should be done) – it’s the most critical component of a complete data strategy, but one that’s often overlooked.
Generative AI has emerged as a way to unlock the value hidden within this information. By using AI as an intelligence core, banks can cut down on inefficient information sourcing while providing their employees with an unprecedented amount of context. This puts time back in their day to focus on what really matters: serving their community and building relationships with customers.
Kevin Green, Chief Marketing Officer at Hapax
Kevin Green is the Chief Marketing Officer at Hapax, where he leverages more than 20 years of experience in marketing, product, and sales leadership to drive innovation in the banking sector. Prior to Hapax, Kevin was the President and CMO of Truent, spearheading initiatives to enhance business interactions with financial institutions through advanced AI-driven platforms. His extensive background includes key roles at Dell Technologies, where he led global digital marketing strategy and innovation, and projekt202, where he guided digital transformation efforts. Kevin's strategic insights and leadership have consistently fostered growth and transformation across various industries. He holds a Bachelor of Arts in Mass Communication, Journalism & Media Studies from Franklin Pierce University.
