WealthTech Insights #28 with Larry Shumbres: Robo-Advisors and Machine Learning for Industry Growth

As part of our series of interviews with industry influencers, I talked to Larry Shumbres, CEO of Totum Risk. He has been in FinTech for over 20 years.

Larry Shumbres
CEO at Totum Risk.

Larry got his feet wet in the technology space and financial sector by working at IBM right out of college. Later, he moved into the investment space working for New York Life MainStay Investments, then Morningstar and Charles Schwab. Larry gained considerable experience building FinTech products and selling them to advisors at eMoney Advisor (acquired by Fidelity Investments) and eVestment (acquired by Nasdaq).

In September 2017, Larry accepted the CEO position at Totum Wealth. At the same time, Totum Wealth was renamed Totum Risk, reflecting the company’s shift in focus to calculating risk.

We talked about the explosion of robo-advisory services and how financial advisors should implement this technology. Larry shared his thoughts about artificial intelligence (AI), machine learning, and blockchain, and his expectations about their impact on the industry.

Robo-advisors and human touch

Talking about the trends that we have lately been observing in the wealth-management and investment industry, Larry agrees that there has been an explosion of robo-advisors. However, he highlights that individual investors aren’t leaving their advisors.

“The individual investor wants that technology, but they also want that personal touch with the advisor.”

Thus, Larry believes that this situation means that financial advisors need to implement technology in the workflow. This is where the challenge of selecting the right technology comes in. Technology should pull together all the market data needed by advisors and investors, have a simple user interface, and allow seamless integration into the workflow.

“FinTech firms should have open and rest APIs for seamless integrations that will make the workflow more efficient for the advisor.”

Integrations give both sides advantages—investors have the technology and the advice they need, while RIAs offer transparent and competitive advice and customer experience.

From AI to blockchain

Another significant trend Larry mentions relates to big data analytics and AI. Advisors can really benefit from having access to all kinds of market data and being able to use that in sophisticated algorithms and approved methodologies.

“Machine Learning and AI will be a huge value add to helping an advisor know more about a client to help them reach their investment goals.”

While a lot of the large institutions, banks, and investment companies are working with and talking about blockchain and cryptocurrencies, Larry says that massive implementation of these technologies will happen in the next several years.

“I do see blockchain coming into play and having an impact on the industry, but I believe that it will probably be a year or two before we see a big shift in that direction.”

Machine learning used for risk assessment

Discussing the ways to make wealth management more efficient, Larry mentions that machine learning may be applied to all areas, whether it is portfolio optimization, chatbots, or risk assessment.

“On the risk side we’re already doing that. We’re pulling data from other FinTech providers to pre-populate the questions and update the scores.  We also have 100 variables built into our models based on life events that when triggered will automatically update the risk capacity score. The more data we pull, and if it fits that variable, the more we’re learning about that client.”

Larry claims that unlike a credit score, which is based on five common factors, Totum Risk uses over 100 variables for risk assessment.

“Are they married? Are they single? Are they divorced? Are they separated? All four of those are different, and they’re going to have a different risk assessment that will update their risk capacity score.”

Larry considers that gathering more data, having more users in the system, and having more integrations, together with machine learning, will all be great for financial advisors and individual investors.

“We tested this with millennials and they loved the idea of having the risk assessed for them when they sit down with an advisor, instead of going through the old-school, typical psychological risk preference questionnaires, which really are meaningless.”

According to Larry, Totum Risk has created a system that enables any investor to just type in their name and phone number to see their risk score.

“But not just the risk score. We’ll have a full narrative explaining how we got to that conclusion. And that’s all be based on machine learning.”

However, Larry expects a significant challenge to arise in machine learning implementation. Academic quantitative models need to be built for multiple scenarios, where the data is updated and run through the models.

“If you don’t have deep academic quantitative models with a tested algorithm then you just have a bunch of data.”

This is why it is important to make sure that information in the data sources used is up to date. Larry says that think if data is not updated at least annually then it’s old data, and the result is not going to be accurate.

Future expectations

Larry believes that in the near future we will see further growth of AI and blockchain in the financial advisory and wealth-management space.

“But again, the products that are using AI have to have those quantitative models and algorithms set up that are true and accurate.”

The biggest challenge Larry sees is in finding people who have experience working in the financial industry as well as a deep technology background. As an example, Larry suggests that AI models should be built by PhDs with relevant expertise in the industry.


Interviewed by Vasyl Soloshchuk, CEO and co-owner at INSART, FinTech & Java engineering company. Vasyl is also author of the WealthTech Club, which conducts research into Fortune and Startup Robo-advisor and Wealth Management companies in terms of the technology ecosystem.

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