The Hubbis Malaysia Wealth Management Forum 2026 served as a platform for Damien Piper, Executive Director of Growth at Unique AI, to spotlight the evolving role of agentic AI in the financial sector. Piper explained that while generic AI tools can enhance productivity, they often do not meet the complex needs of financial institutions.
He emphasized a significant shift from generic applications to more advanced, workflow-focused implementations. Piper detailed how agentic AI can improve operations in wealth management, asset management, and investment banking. He argued that AI's value is realized only when it is precise, secure, and integrated with relevant data. This is especially important in regulated environments where accuracy and compliance are crucial.
The Challenge of Generic AI
During his presentation, Piper took a strong stance against the one-size-fits-all approach of generic AI. He remarked, "You cannot just drop a generic AI tool into a wealth management environment and expect it to understand the business; the documents, the data, and the workflows are too specific." This statement underscores the limitations of standard AI tools, which may perform well in general productivity but struggle with the complexities of financial documents and client portfolios.
Piper highlighted that precision is essential for adoption. He noted, "Without precision, there is no adoption. Without adoption, there is no business value." This idea resonates in a sector where the stakes are high and trust is essential.
Overcoming Barriers to Adoption
One major barrier to AI integration in finance has been the issue of 'hallucination,' where AI systems generate inaccurate or nonsensical outputs. Unique AI has addressed this by implementing hallucination-checking protocols and prompt-extension engines that ensure the technology is reliable in regulated environments. These advancements are key to building trust among financial professionals who depend on accurate data.
Piper pointed out that AI's utility significantly increases when it can interact with client relationship management (CRM) data, portfolio systems, and other internal documents. He stated, "Client data is essential to real use cases," suggesting that AI's effectiveness is directly linked to its access to relevant data streams.
Supporting Relationship Managers
Rather than seeking to replace relationship managers (RMs), Piper emphasized that AI should enhance their capabilities. He proposed the creation of agentic workflows that assist advisers in executing specific tasks, thereby boosting their productivity. This approach is particularly relevant as the financial sector faces the challenge of maintaining strong client relationships while embracing technology.
Security and Deployment Considerations
Piper also stressed the importance of security and deployment models in wealth management. Due to the sensitive nature of wealth and client data, generic cloud solutions often pose risks. Unique AI advocates for customized guardrails and secure zones to protect client information, which may require on-premise deployment strategies. Such measures are vital for ensuring compliance and maintaining client trust.
Multifaceted Applications of Agentic AI
The potential applications of agentic AI in wealth management are vast. Piper outlined various use cases, including investment insights, Know Your Customer (KYC) processes, onboarding, meeting summaries, fund analysis, due diligence, RFP drafting, reconciliation, and compliance checks. Each area benefits from AI's ability to process large volumes of data accurately and efficiently.
As the financial industry evolves, Piper’s insights at the Hubbis Forum highlight a critical shift toward addressing the specific needs of wealth management. The demand for precision, security, and integration of client data will shape the future of AI in this sector. The journey towards a more intelligent and responsive financial ecosystem is underway, with agentic AI at its center.
Quick answers
What is agentic AI?
Agentic AI refers to AI systems that can perform specific tasks and workflows, particularly in complex environments like wealth management.
Why is precision important in AI for wealth management?
Precision ensures that AI outputs are accurate and relevant, which is essential for user adoption and business value.
What challenges does Unique AI address in financial institutions?
Unique AI focuses on issues like hallucination in AI outputs, security of client data, and integration with existing financial systems.
How can AI augment relationship managers?
By automating routine tasks and providing insights, AI can support RMs, allowing them to focus on building client relationships.
The stories that move AI & crypto markets — before the market reacts.
Free. 7am ET. Five stories. 62,400 readers.
