The joint McGill- Desautels and Rotman (University of Toronto) Risk management and Financial Innovations conference took place in Fairmont Mont Tremblant, March 8 – 10, 2019. The conference was in memory of Peter Christoffersen and meant to follow up on his research interests and ideas. Peter always kept close link with industry. His research attracted a lot of attention from asset management and banking professionals. FinTech became his most recent agenda.
FinTech is relatively new for academics. It has been however one of the major trends in financial services and capital markets sectors. To learn more about past, current and future trends in FinTech applications, we organized industry round table on “How AI and Data Analytics are changing Financial Services “
The panel moderator:
Stephen Hui, Partner & Portfolio Manager, Pembroke Management Ltd
Jean-Francois Courville, CEO, WealthSimple for Advisors
Randy Cass, CEO Founder Portfolio Manager, Nest Wealth
David Nault, General Partner, Luge Capital
As far as an average investor is concerned about FinTech, robo-advising cannot remain un-noticed. We were lucky to have Jean-Francois Courville and Randy Cass, CEOs of one of the biggest Canadian robo-advising companies on the panel. David Nault is a VC fund manager who invests exclusively in Fintech companies. Finally, the moderator, Stephen (Steve) Hui, comes from traditional asset management. He was in perfect position to challenge “newcomers”.
Summary of Discussions:
What are AI applications in financial services (robo-advising)?
We define AI as everything which involves machine learning from various data sources, training algorithms to self-correct and make decisions. It is still often confused with extensive data analytics, or big data. While both can overlap, as far as a final decision is made by a human, we refer to it as data analytics.
For robo-advisors AI has very little applications. It is predominantly a technology (or you can call it an app) which allows you to make passive asset allocation of your investments. As a matter of fact, AI would contradict to the whole principal of robo-advising.
The main attractiveness of robo-advising is transparency and ability to be in control. While the asset allocation is done by a model which is built in an app, it is client’s risk preferences which guide the app and ultimate asset allocations. Thus, transparency, accessibility and every day monitoring and control are the main pillars of robo-advising platforms that attract investors.
Overall, as passive portfolios on average outperform active portfolios, robo-advisors are off to a good start. Passive asset allocations is what they provide.
How personalized robo-advisors’ services are? What happens when there are no human interaction?
It takes 5 seconds and $1 to open an account and to authenticate it. It is also hard to argue that getting plain vanilla advice for a small fee is much better then receiving similar services from a mutual funds with 2.5% fees. Thus, the demand, especially from millennials, is high.
Robo-advisors use one human managing 10,000 accounts with the help of technology. The important concern is how good the customer service is.
Interestingly, online digital activity may give robo-advisors more information about their clients needs compared to traditional asset managers. They know you better than traditional advisors do since the moment you open your online accounts. Your digital print: how often you log in, log out, what news you pay more attention to; allows them to learn closely your risk preferences. Given this information, they also intend to anticipate your future needs.
It is similar to booking.com experience. As you for example book a hotel in NYC, instantly bookings.com sends you a bunch of activities happening in New York on your visit dates, and then the follow up email about “what next?”, and suggestions about other tourist destinations.
In case of robo-advisors, they start educating their clients about their financial goals and how to stay anchored on their objectives.
How does the future look like for baking and asset management?
The easily reached conclusions were that in 10 years traditional banking will loose about 30% of personnel as machines are becoming capable to perform some of human jobs. Traditional investment management will have tough times if they do not start adjusting now. The asset management fees will be much lower.
There are great opportunities coming from open banking. It will decrease advantages of incumbents (traditional banks), and will level down the information asymmetry.
How big are AI solutions these days?
“Worldwide spending on cognitive and AI systems was estimated to reach $19.1 billion in 2018, an increase of 54.2% over the amount spent in 2017. With industries investing aggressively in projects that utilize cognitive/AI software capabilities, IDC forecasts cognitive and AI spending to grow to $52.2 billion in 2021 and achieve a CAGR of 46.2% over the 2016–2021 period (source: Medici) “
“According to Nasdaq, in 2018, JPMorgan Chase had a $10.8-billion tech budget, with $5 billion set aside for new investments. JPMorgan’s treasury services division handles an average of $5 trillion daily in everything from payroll and remittances to multi-billion-dollar merger checks, and the bank wants to bring AI into this game. The bank was teaching its machines about its clients so AI can start anticipating their questions and needs. The Bank of America has also made its AI debut with Erica, who leverages predictive analytics and cognitive messaging to provide financial guidance to over 45 million customers. (source: Medici)”
AI is definitely the future. What are the limits and applications of AI in Asset management and Capital Markets overall? This is to be seen – to try to take a first look, we are organizing another conference: “Applications of AI and Machine Learning in Capital Markets and Risk Management” on April 3, 2020 at McGill – Desautels.