Imagining advanced analytics and robo-advice

February 22, 2017

A blog post by Doug Dannemiller, Investment Management research leader, Deloitte Services LP and Rohit Kataria, senior analyst, Deloitte Support Services India Private Limited

Science fact. Some leading robo-advisers are beginning to leverage advanced analytics to build retirement plans that inform clients about how much they will need to save to retire according to their target retirement age and lifestyle. These advanced plans can also budget clients’ monthly saving and discretionary spending amounts. These leading robo-advisers are linking a full spectrum of client financial accounts to capture, track, and classify historical financial transactions to make the budget and retirement recommendations. This is a good initial step for utilization of this client data, but it pales in comparison to the services that can be imagined with these real-time records of finances and transactions.

Possibilities ahead

In the digital world, all the data about where we go, what we buy, where we dine, who we owe, and how we invest act as markers that we leave behind as we journey through life. With the help of advanced analytics, this data could be analyzed to identify the unique spending and saving patterns of each individual or household. Digital advice could become completely personalized, specific, and timely. As algorithms learn more about the client, the digital adviser could potentially make adjustments to client risk tolerances in comparatively tight time frames. This could be done through modeling and cohort comparisons to develop a deeper understanding of transactions in near real-time. Advanced models may even be able to recommend adjusting risk tolerances earlier than would normally happen in an investor/adviser relationship. These tools have the power to identify patterns of transactions that may reveal tendencies about the investor. This information purely used in the investor’s best interest could lead to fast and effective adjustments to the financial profile.

Another possibility, well within the imagination, is the potential to offer individually customized investment portfolios. Deep analysis of transactions has the potential to divulge the clients’ passions, in a detailed and unfiltered way. Investment portfolios that correspond to those passions could be pitched on a mass-produced, but customized basis. Imagine the robo-adviser knowing all your interests and the investment possibilities that align to each of them. The potentially unique portfolios created by advanced algorithms could incorporate individualized themes (space exploration, agriculture, impact investing, others) and still meet the investor’s individual risk and investment parameters of traditional portfolios such as time horizon, expected volatility, and yield. The robo-adviser might also be able to calculate the likelihood that an investor will exchange his/her current portfolio for its individualized approach, based on the strength of the investor’s passions and degree that the portfolio matches them combined with prior investor results.

However, a potentially scary scenario could come from an advanced analytical review of all these transactions. This sort of detailed information misused is the fuel for a trip to the twilight zone, a place where investor data is breached or used in a way contrary to the customer’s best interest. This level of detailed information is the essence of personally identifiable information and could be used to manipulate or control customers. Such is the potential for the combination of complete financial transactions and advanced analytics.

Avoiding the twilight zone

Robo-advice is one of the more promising approaches for bringing 21st-century technology to the mass-affluent investor segment. It may very well be the dawn of a new day for investors. However, in order to avoid being trapped in the twilight zone, robo-advisers should focus on risk management. Operational risk and investment risk are among the top priorities to manage as advanced analytics are deployed to use customer financial data and transactional data to develop tailored investment solutions. Investment management firms that develop these solutions early may have a distinct advantage gathering assets in technology-centric market segments, but those that develop hasty solutions without the appropriate controls are likely to see only short-term success at best.