Interview with Richard Hamm, Bristol Gate Capital Partners
Artificial intelligence is disrupting the finance industry
Amazon and Google are investing heavily in artificial intelligence (AI), and the future looks bright for new applications in healthcare, finance and practically every other industry. According to research by Autonomous Next, 2.5 million financial service employees are exposed to AI technologies. In addition, AI will have an estimated $1 trillion impact across banking, investment management and insurance. But is the finance industry ready to embrace AI?
Machine learning, a branch of AI, is changing the landscape of how financial advisors invest and, by proxy, how they interact with their clients. In the future, smart algorithms and big data will do most of the “heavy lifting” that humans used to do. This includes front, middle, and back office automation and improvement.
• Front office—chatbots, communication platforms and interfaces
• Middle office—regulatory compliance, workflow automation, APIs, fraud prevention
• Back office—analytics, investment strategies, credit, insurance, trading
These changes will have a deep impact on how financial advisors look at data and how they make informed decisions based on this information. Machine learning is already here, and regardless of the timeline advisors who are willing to embrace, learn from and adapt to new methods will have an obvious edge over competitors. According to Richard Hamm, executive chairman of Bristol Gate Capital Partners, there isn’t an advisor alive that doesn’t want to make more money and fintech (with AI) will be the tip of the spear that advisors will utilize for maximizing profits.
Man and machine working together: Empirical advice
Financial advisors are always looking to improve and streamline their workflows, the most important one being choosing what to put in a portfolio while accounting for risk. Most advisors are content with firing up a portfolio and following set strategies based on certain investment models, but Hamm believes in building portfolios through rigorous testing. When he and his team identify a portfolio that is ready for capital, they put money into it and run the test portfolio in parallel with the actual portfolio.
Evidence-based portfolios built on data are the only way to go because without evidence, it’s a toss-up as to whether it will be beneficial.
“We think everything we do should be tested thoroughly; you should have evidence, and, if [it works], then you can actually put it into real life,” Hamm says.