Over the past ten years the power of machine learning has been harnessed to provide automated investment advice at low costs through robo-advisory platforms. Robo-advisors are digital platforms that use machine learning to guide customers through an automated investment advisory process. Similar to traditional financial advisors, robo-advisors allocate a client’s assets based on risk tolerance and target returns. However, robo-advisors differentiate themselves by using machine learning algorithms to guide customers through an automated investment advisory process with limited human intervention. The market is sizeable and growing; estimates suggest the U.S. market potential for robo-advisors is around $400 billion.
However, traditional investment management services and most robo-advisors have been designed to meet the needs of men. Today, 86% of investment advisors are men, with an average age of above 50 years old. Studies find that 73% of women are unhappy with offerings in the financial services industry, despite women controlling $5 trillion in investable assets. On the whole, investment management tools today fail to account for key differences in the financial lives of women, such as how much women earn and the timing of their earnings. Factors like pregnancy and child raising lead women to take more career breaks, the gender pay gap affects women’s overall earnings, and on average women’s salaries peak earlier in their careers as compared to men’s salaries. In addition, women live six years longer on average, and therefore need to think about investing over a different time horizon.
To address these realities, Ellevest’s platform has developed a proprietary algorithm powered by machine learning technology and tailored specifically to women’s incomes and life cycles. The company’s algorithm differs from competitors in that it considers factors such as earnings trajectory and anticipated career breaks. It provides women with an investing strategy based on the life events they indicate as most important such as: starting a business, having a child, buying a home, starting an emergency fund, or retirement. Ellevest’s investment management platform is based on the notion that women fundamentally need to make different investing and saving decisions than men.
The Ellevest business model is in many ways similar to its “gender neutral” competitors such as Betterment or Wealthfront. It develops investment portfolios for clients using primarily ETFs, with no minimum deposit required. The platform offers multiple options based on fit with age, goals, risk tolerance and other factors. Recently, Ellevest updated its fee structure to be at parity with Wealthfront and Betterment, both of which charge a 0.25 percent fee for digital-only investment services. In addition, Ellevest offers premium services at a higher fee of .50 percent which includes 1:1 executive coaching and personalized financial guidance.
The next critical step for Ellevest is to demonstrate that its algorithm can indeed generate superior financial and investment outcomes for women. In order to do so, customer acquisition should be a primary focus in the short to medium term. The Ellevest platform will need to continue to grow its user base such that it can draw conclusions as to whether or not its algorithms’ factors are indeed the right ones to optimize financial outcomes for women.
Over the long term, Ellevest may need to refine its product offerings. Studies suggest that women prefer to receive interactive financial advice with the ability to ask questions with personal interactions. However, Ellevest’s premium services are more expensive that those of competitors. For example, Betterment’s Premium option, which includes a team of financial advisors available via phone and email, costs only 0.40 percent compared to Ellevest’s 0.50 percent fee. Over time, the 0.10 percent difference can make a huge difference in the wealth accumulation of women, who already face the gender pay gap earing 0.70 percent of what male counterparts earn.
Looking ahead, critical questions remains unanswered. Can updated algorithms truly provide superior investment advice and outcomes for women? More broadly, is it possible to use machine learning to reverse gender biases in investment management?
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