BetaNXT CTO: Don’t Rush Your AI – Plan The Long Game

Tech Expert Says Avoid The Hype, Implement AI Solutions With A Long-Term Strategy, Develop Trusted Data And Safeguard Client Privacy
Don Henderson, Chief Technology Officer, BetaNXT
Don Henderson, Chief Technology Officer, BetaNXT

With the blistering pace of AI innovation in wealth management, firms of all kinds face pressure to develop and implement solutions quickly to stay ahead of the competition. With a rapid pace of deployment, however, firms may run a risk of getting over their skis with systems that aren’t ready for safe and efficient use.

To explore the pitfalls and safeguards of implementing AI solutions, we spoke with Don Henderson, Chief Technology Officer of BetaNXT, which provides wealth management enterprise solutions and data capabilities.

Henderson says that advisors and firms need to avoid the hype around AI and develop a long-term strategy. He explains the risks of rushing too quickly and how firms can guard data privacy and consumer protection while implementing AI solutions.

WSR: What is the realistic promise of AI for financial advice and what is the hype? How is it dangerous to believe the hype?

Henderson: The promise of AI in financial decision-making lies in its ability to enhance productivity, streamline communications and provide hyper-personalized next steps. It enables advisors to visualize alternative scenarios in real time and engage with clients on complex portfolios efficiently.

There are two points to keep in mind with regard to AI. First, automation will reduce the human error of today’s advisory model, so incorporating AI into financial advice will make the process better and faster. Second, AI in the future will allow scenarios to be based on more information, so the next steps advisors can recommend will continue to improve.

Rushing into AI without ensuring data integrity can lead to unreliable results and potential regulatory issues.

Don Henderson, Chief Technology Officer of BetaNXT

However, the hype surrounding AI can be dangerous if not approached with care and caution. Effective AI implementation requires a strong foundation in data architecture and well-tested algorithms. Rushing into AI without ensuring data integrity can lead to unreliable results and potential regulatory issues, as reflected in SEC Chair Gary Gensler’s recent statements on AI-washing, and the danger of overreliance on a small number of AI algorithms.

WSR: How do firms construct an effective long game for AI? What is the right way to develop and implement it?

Henderson: It’s crucial for businesses and organizations to recognize that AI is a long-term game. First, companies need to have a plan with comprehensive policies before they invest in data architecture – this includes all types of data, both structured and unstructured. They will need to invest in new tools and processes, and they need to have strong governance to monitor the access and the outputs. They also need a clear definition of company policies and use cases.

Rather than expecting immediate returns, a sustained commitment to research, development and collaboration is necessary. Investing in talent, fostering interdisciplinary collaborations and staying abreast of the latest research are essential components for navigating the evolving landscape of AI.

Companies that invest the necessary money in updating their technology, systems and teams will see the benefits faster.

WSR: What are the risks to rushing your AI processes forward, and what needs to be in place first?

Henderson: AI is only as good as the data that serves it. If you don’t understand the data you’re using, you could create situations where sensitive information is inferred from harmless data that is stored.

AI requires a lot of data to be effective, and we can’t achieve the real objective of trusted answers until AI has the trusted data that it will need to reach those answers. Because there is a rush to keep up and be on the crest of the wave to be the first to implement AI technology and layer it into your tech stack, the underlying data issues, especially within the wealth management space, are staying the same.

The challenge is creating data that is trusted. It requires a lot of work to tag all the historical data necessary to enable AI to learn fast, otherwise there will still have to be manual/human value-adds to help AI learn. A strategic approach to AI implementation requires a meticulous examination of data integrity issues, focusing on addressing underlying causes before layering on an AI strategy.

The challenge is creating data that is trusted.

Don Henderson, Chief Technology Officer of BetaNXT

WSR: How can data privacy and consumer protection be maintained with the rapid pace of AI innovation?

Henderson: Data privacy and consumer protection can be maintained amid the rapid pace of AI innovation through a comprehensive data architecture that covers all aspects of the advisor, investor and enterprise. This architecture allows for swift identification and response to privacy gaps as part of a fully digitized, end-to-end wealth management system with a well-curated data set. Such a setup enables faster and more reliable detection of privacy issues compared to traditional manual processes.

For example, we provide an integrated security master and reference data platform to secure valuation, pricing and settlement processes, with automatic flagging and correction of any erroneous data. We also use a multi-cloud data integration strategy encompassing secure data hosting and transparent privacy policies with consent mechanisms, designed to ensure robust data privacy and consumer protection in the era of AI innovation.

James Miller, Contributing Editor & Research Analyst at Wealth Solutions Report, can be reached at

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