AI promises alpha generation, operational leverage, and competitive edge in Asset and Wealth Management (AWM) from hyper-personalized client advisory to real-time risk mitigation and seamless compliance. Yet, without the right data architecture, most AI initiatives falter in production, plagued by hallucinations, siloed insights, and regulatory pitfalls. This is where knowledge graphs and graph databases step in as the critical enabler, transforming fragmented data into interconnected, context-rich networks that fuel precise, explainable AI.
About the Authors
Vladimir Agajanov
Director, Head of Data Science
Vlad is a seasoned leader and solution architect in AI, data science, and machine learning for the financial industry. With diverse expertise spanning generative AI, machine and deep learning, data engineering, product architecture, and operational support, he excels in crafting scalable, robust solutions such as advanced AI tools, marketing analysis models, and risk assessment systems. Leveraging full-fledged product building capabilities with thorough understanding and hands-on experience in every aspect of the product, he is backed by deep education from top schools and extensive resources in AI and related fields.
Vlad is adept at guiding cross-functional teams to transform complex business challenges into practical, compliant strategies that enhance efficiency in investments and finance.
Daniel Paige
Manager, Solution Architect
Dan has 10+ years of experience within the finance industry and client services. He has experience working with OMS/IMS software (CR IMS, Aladdin, and more) in a wide range of roles - including Operations, IT, Implementation, and Product support/development.
Well versed in the market data and analytics industry, Dan adds a breadth of knowledge in relation to vendors such as ICE, Refinitiv, Markit, Bloomberg, etc. With his background in client facing roles, Dan has a knack for delivering tailored solutioning for clients.