AI hype is everywhere. But in financial services, cutting through the noise to focus on what delivers real business value is essential, particularly in the financial services industry. Banks and other financial services firms need to pay particular attention to the use of data and how emerging AI capabilities are applied. 

Banks and financial institutions operate under intense regulatory scrutiny. Every investigation, regulatory filing, and customer decision must be explainable, transparent, and defensible.  That’s why the path to AI adoption, particularly in areas like anti-money laundering (AML), must be thoughtful, trusted, and built on a strong data foundation. 

The industry is seeing rapid increase in the use of AI for internal processes like HR workflows and customer self-service; however, when it comes to regulated domains, the pace is understandably more cautious.

What is Quantexa’s view? Financial institutions should focus on immediate, practical use cases that deliver value today, while laying the groundwork for more advanced adoption tomorrow. 

GenAI: Immediate value with guardrails 

GenAI is becoming widely adopted outside of financial services for a variety of use cases from research summarization to legal notations. However, application in a regulated environment should be approached cautiously.  As GenAI capabilities mature and methods become more transparent, we expect wider adoption in AML workflows, especially when solutions are designed with compliance and accountability at their core.

Within AML, FIs should be looking at use cases like investigations summarizations, as provided in Quantexa's Q-Assist tool. Q-Assists allows an investigator to automatically summarize a case, while still giving the capability to peruse source data and documents to confirm the accuracy of the machine generated summary.  In AML, this helps investigators quickly document complex cases thoroughly and accurately.  

Agentic AI: Emerging but promising 

Specific to Agentic AI, AI agents that can reason, plan, and act are gaining traction in financial services for low-risk use cases, like automating password resets or handling routine customer service tasks. 

In AML, financial institutions should be evaluating first steps in deployment of Agentic AI; however, the industry is not yet ready to operate independently for regulated workflows like AML. The technology is ready, however no financial institution has yet tested it's minimal explainability and governance needed to meet regulatory standards. Explainability and model risk approval is on the near term horizon.

Understanding the current environment and near term limitations of AI technologies, Quantexa is taking a forward-looking approach. Using Quantexa's Q-Assist tool, our customers can deploy GenAI functionality now, with the confidence that the solution will scale as Agentic AI becomes more trusted and compliant in the years ahead. 

The need for trusted, scalable AI in regulated industries 

Data is the foundation of any AI solution - garbage in, garbage out. Thus, as many financial institutions still struggle with siloed data, fragmented systems, and manual processes, the lack of trusted data could hinder the potential gains of using AI. Entity resolution creates a connected and trusted data foundation, which is the basis of creating context. Without context, AI models often fail to produce accurate or trustworthy results, especially when decisions carry legal or financial consequences.  A strong data foundation helps ensure decisions are based on a full, accurate understanding of the financial institution's customers, counterparties and transactions. 

The future of AI in financial services hinges on trust, transparency, and context. This requires a platform that is able to create a connected, contextual view of your data - a data fabric - built using advanced entity resolution and relationship intelligence. This enables AI models to reason using resolved and accurate information, which drives better decisions and supports outcomes that are both explainable and auditable. 

If you're not exploring AI, you're falling behind 

Financial institutions need to look beyond AI experimentation and prepare for enterprise-scale adoption, with the appropriate guardrails in place. From GenAI-powered investigation support to future-ready Agentic AI workflows, organizations are seeking to unlock value from AI without compromising on governance or risk.

AI is already driving measurable impact in financial services, and its role in regulated domains is only growing. Firms that are not actively exploring or piloting these technologies risk missing opportunities and being left behind.  By starting with safe, explainable use cases, like GenAI-enabled summarization, and building on a trusted data foundation, financial institutions can adopt AI with confidence and prepare for what’s next. 

Find out how other organizations are already seeing results.