When I first graduated from Stanford, I’d tell people I have a degree in artificial intelligence and they would reply, “so, you are a science fiction writer?” I would then explain that I was an engineer who studied linguistics, cognition, statistics and algorithmic problem-solving. Their brows would furrow more. Today, AI is hot topic, and the entire financial industry is trying to find ways to leverage it. After a decade in quantitative finance, I decided to return home to AI and build a new kind of wealth management firm – even different from other robo-advisers.
Studying AI at Stanford was an absolute blast. Marissa Mayer – yes, that Marissa Mayer – was my CS106B: Programming Abstractions in C++ professor. She pushed our class really hard and imparted her world-famous work ethic. I was also fortunate enough to study machine learning under Andrew Ng, one of the founders of Google Brain. These mentors and others helped to open my mind to ideas and possibilities that I could have never imagined previously.
After graduating, I explored different job options in software, military applications, film and finance. Ultimately I settled on becoming a quant finance consultant because I was able to put my machine learning skills to work understanding real-time data that explained the world around me.
In college machine learning is fun, because the data is provided up front, sparkling clean and easy to use. In the real world, however, you have to aggregate multiple sources and deal with annoying errors and exceptions. I learned that true data science involves a lot of shovel work, and you have to get your hands dirty.
As the years went on, I built factor-based stock selection models for the U.S., Canada, Europe and Asia. Creating a global model was a challenge, because you can’t always compare apples to apples. Different corporate reporting and national market dynamics require critical thinking about fundamentals and currencies. I learned that the craft of quant finance is both art and science, and you have to be creative to extract narratives from the data.
After the crash of 2008, macro analysis gained prominence and I was tasked with building asset-allocation models based on global economic and market factors. The work was fascinating, because I got to view historical events through the lens of data. Bond yields in 1987, Japanese factory orders in 1989 and credit spreads in 2008 all told stories about dramatic market events.
Studying long-term macro trends through the data showed me that the markets were not by any means rational or efficient, and that understanding history by the numbers is the most important discipline in financial services, especially for traders, asset managers and wealth managers.
After the better part of a decade, even though I found my work intellectually stimulating, I started to feel like I was running in place. For all of the cool research that I was doing, what did it matter if the clients never really got to see what I was seeing? Seeing the lasting impact of the Great Recession, I also lamented that regular people got the short end of the stick, and I had serious concerns about the ethical impact of my profession.
At the same time, I noticed a disconnect between the fees being charged in my business and the value being added across various parts of the investment supply chain. I saw technology and process inefficiencies everywhere, and it occurred to me that there was a better way of doing business. Around that time I started to think of a new business model.
My vision was to take the investment process I was building for institutions and high-net-worth individuals, and make it available to folks who only have modest savings. My other goal was to make it easy for people to sign up and manage their accounts, and also to do it all for a fee that is reasonable.
My consulting partner Chris Sanford, the co-founder and CTO of Responsive Capital Management (a.k.a. Responsive.AI), and I went to a local bar to discuss this new idea. We both agreed that we needed to strike out on our own and do something exciting. We weren’t getting any younger. We decided at that moment to build our own wealth management company from the ground up, putting the client first and technology front and center.
Responsive.AI was born over a couple of beers, but it took more than a year to get it off the ground. Chris and I had to navigate regulators and develop business processes for compliance, the back office and trading. We had to find a portfolio manager and we had to tell our story to as many people as possible. There were so many hats to wear, and although it felt overwhelming, it was strangely liberating: We were creating our vision our way, and we were dead set on creating a radical, disruptive investment service.
Today we are welcoming clients with $10k or more of investable assets to our website and mobile platform. We even have a chat bot that can answer some basic questions. The research we do goes straight out to clients over Facebook and Twitter in a language they can understand. Even better, we are doing this all at a fraction of the cost that our old-school competitors charge investors.
Had I stuck with quant consulting, I may have been more comfortable, but I would have been missing out on the chance to put my ideas into action and to build something that has the potential to change the wealth management industry. The Responsive team is very excited about the potential for fintech to create better services and tools for clients and professionals alike.
Whether it’s chat bots keeping clients informed and up to date or AI helping financial advisers to do a better job, we think the future is bright for financial services professionals and organizations that embrace the changes that fintech is bringing as it evolves.
Davyde Wachell is the founder/CEO of Responsive.AI, a fintech startup that offers a digital investment advisory service.
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