Given Artificial Intelligence (AI) is widely used across many industries, how does it apply to wealth management?
More importantly, is there a common definition of AI for financial services?
In speaking with various professionals in the industry, it is clear that most firms employ some version of AI but struggle to define it. With that being said, many are using AI capabilities that have proven to be useful to the advisor-client relationship and overall portfolio performance.
AI can help professionals in financial services recognize patterns, apply defined rules, and make better-informed decisions in both operations and relationship management.
While AI certainly plays an important back-office role and can improve logistics, we are nowhere near the days of AI completely replacing the role of an advisor. Maintaining the human aspect to an advisor-client relationship is crucial. Building a portfolio specialized to your goals and lifestyle will always collaborate with algorithmic investing to provide the best client experience. However, AI tools provide an opportunity to simplify and streamline processes, particularly for back office operators.
Data Processing and Analysis in Financial Advice
AI’s biggest value add is its ability to process data and produce human-consumable analysis. This data serves as the bedrock for most advisor’s recommendations and financial advice. Companies are implementing several AI based tools within their technology stack to that end, including market analysis solutions and financial models, which can help game-out the potential of a certain investment.
Advisors are deploying machine learning to automate back-office processes as well, which provide tremendous value to short-staffed firms. RPA (Robotic Process Automation) can speed up transaction driven tasks, handled by bots instead of human operators, which drastically improves workflows and limits bottlenecks. Imagine taking a paper form (yes they are still in existence in our industry) and having it be processed completely electronically, without human interaction. Systems like Docupace, which automate transactional workflows, process millions of physical data points into digital copies yearly. Everything from New Account Openings to Maintenance requests can be done automatically, and with very limited human involvement. Firms like Voya and Kovack Securities are paving the way with implementations of the RPA in their back office operations. While there is a lot of experimentation involved in determining the right data set, or the most time consuming tasks, the benefits are clear. The progress in applying machine learning tools to operational workflows are immediately visible and easily measurable.
The Future of Wealth Management and AI
With AI tools maturing and becoming more wealth management firm friendly, the adoption of this technology should continue to increase. The examples above are just a few use cases for implementing machine learning. Surveillance and Compliance is another area where rules based intelligence can provide significant value. AI technology can dramatically reduce NIGOs and flag issues ahead of time, correcting potential problems on the front end of the transaction, thus reducing costs, enhancing speed, improving the quality of service and simultaneously protecting advisors and their firms.
It will be an exciting couple of years as we continue to see new AI-based solutions advance and come to life. These solutions will help wealth management firms drive efficiencies and provide better services to clients. Whether it is on the front end of the process or the back end, the influence of AI-based tools will continue to make a significant impact.
While we are far from the futuristic implementations of AI as depicted in Hollywood movies— “iRobot,” or “Terminator” to name a few—the realities of how the technology will influence our lives is very real. The application of AI in wealth management will only continue to amplify performance and transform the industry.