The advances in computing capacity and artificial intelligence have long been hailed as a tremendous efficiency boost for wealth advisors and their clients.
While almost all advisors recognize the implicit value of integrating AI into their daily practice, most AI adoption has either been via robo advisors (Wealthfront and Betterment) or “intelligent” customer service (think chatbots).
The rise in model portfolios and manager selection has relieved advisors of the sole focus on individual equities, but clients still want insights on their favorite names (TSLA, AAPL, BTC). In addition, advisors with in-house research need to be able to provide the full context behind analyst upgrades/downgrades or price target changes and, therefore, have to dig through a mountain of PDFs and collate information from disparate sources in order to answer client questions and be seen as value-add.
In the past, advisors with access to a dedicated information terminal could quickly access news and data their clients weren’t seeing. However, over the years, the commodification and proliferation of news and information (including *cringe* “fake news”) has meant that clients usually have access to the same information as their advisors. This shift means that the value of premium services now comes from mining terabytes of data to find what is actually impacting a stock or bond. In other words, the value proposition advisors offer is to turn raw data (news, market prices) into insights tailored to each client’s portfolio.
More companies are offering solutions to navigate this space, taking advantage of cloud computing to narrow down news stories to those that will likely impact the trading activity of the underlying asset. AI goes one step further, in providing the necessary inputs to allow an advisor to not only explain what's moving the stock but also what is likely to come next. The same process is being leveraged with model portfolios as advisors look to reshape allocations. Rather than painstaking manual work across spreadsheets and programs, advisors are using click-through software to effortlessly test how different weightings will react across all kinds of scenarios and macro environments.
This ability to leverage machine learning to track client portfolios in a scalable manner becomes invaluable as advisors manage upwards of 200-plus client relationships, while also balancing increasing business development and back-office responsibilities.
The future of the industry must be in relying on your computer to remind you who to call, when and why. The analysis machines produce can lead to compelling insights to your clients on a scale unimaginable if done manually. That is the clear use case winner for AI in wealth management.
The doomsday predictions that robos would threaten the livelihood of financial advisors have ultimately proved false, but it’s increasingly clear that advisors who quickly and intelligently integrate AI into their practice are rapidly gaining an edge on the competition.
Jan Szilagyi is CEO and co-founder of Toggle. He is a former quant trader at Duquesne Capital.