It’s all about context. Scaling up agentic AI for anti-financial crime.
Banks are operationally challenged by volume and depth in financial crime compliance. Neil Katkov, PhD, explores how modernizing data infrastructure and embracing contextualization can support anti-financial crime operations & AI initiatives in a demanding regulatory environment.
Inside the report
- Data modernization for AI enablement: Why fragmented data and weak governance are now the primary barriers slowing AI deployment.
- Optimizing agentic AI: How siloed data complicates operations and leads to missed opportunities for detecting illicit activities.
- Enterprise knowledge graphs: How to durably hold the bank's knowledge and assemble the relevant portion as context for each agent, at scale.
“Enterprise knowledge graphs can support agentic AI by holding the bank's knowledge and assembling the relevant portion as context for each agent, at scale.”
— Neil Katkov, PhD
Neil Katkov, PhD
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Introduction
Organizations can improve their financial crime posture by providing agentic AI with a reliable data foundation and a domain-specific ontology powered by enterprise knowledge graphs.
Modernizing data infrastructure and embracing contextualization can be effective tactics to support anti-financial crime (AFC) operations in a demanding and dynamic regulatory environment. By leveraging context engineering to provide agentic artificial intelligence (AI) with a reliable and concrete data foundation, organizations can improve their financial crime posture, enhancing both compliance and overall operational efficiency.…
