{"id":30669,"date":"2018-11-13T15:24:03","date_gmt":"2018-11-13T20:24:03","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/ellevest-can-machine-learning-reverse-gender-biases-in-investment-management\/"},"modified":"2018-11-13T15:24:03","modified_gmt":"2018-11-13T20:24:03","slug":"ellevest-can-machine-learning-reverse-gender-biases-in-investment-management","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ellevest-can-machine-learning-reverse-gender-biases-in-investment-management\/","title":{"rendered":"Ellevest: Can machine learning reverse gender biases in investment management?"},"content":{"rendered":"

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.<\/strong> 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\u2019s 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.<\/p>\n

However, traditional investment management services and most robo-advisors have been designed to meet the needs of men.<\/strong> 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\u2019s overall earnings, and on average women\u2019s salaries peak earlier in their careers as compared to men\u2019s salaries. In addition, women live six years longer on average, and therefore need to think about investing over a different time horizon.<\/p>\n

Figure 1. On average women\u2019s salaries peak earlier in their careers as compared to men\u2019s salaries.<\/em>
\n\"\"<\/a><\/p>\n

To address these realities, Ellevest\u2019s platform has developed a proprietary algorithm powered by machine learning technology and tailored specifically to women\u2019s incomes and life cycles.<\/strong> The company\u2019s 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\u2019s investment management platform is based on the notion that women fundamentally need to make different investing and saving decisions than men.<\/p>\n

Figure 2. Ellevest\u2019s platform provides women with an investing strategy based on the life events they indicate as most important, such as buying a home.<\/em>
\n\"\"<\/a><\/p>\n

The Ellevest business model is in many ways similar to its \u201cgender neutral\u201d competitors such as Betterment or Wealthfront.<\/strong> 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.<\/p>\n

The next critical step for Ellevest is to demonstrate that its algorithm can indeed generate superior financial and investment outcomes for women. <\/strong>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.<\/p>\n

Over the long term, Ellevest may need to refine its product offerings.<\/strong> Studies suggest that women prefer to receive interactive financial advice with the ability to ask questions with personal interactions. However, Ellevest\u2019s premium services are more expensive that those of competitors. For example, Betterment\u2019s Premium option, which includes a team of financial advisors available via phone and email, costs only 0.40 percent compared to Ellevest\u2019s 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.<\/p>\n

Looking ahead, critical questions remains unanswered.<\/strong>\u00a0Can 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?<\/p>\n

(790 words)<\/p>\n

Sources:<\/p>\n

1. Wallace, Charles. (2017) Machine Learning in Finance: Is a Robo-Advisor Smart Enough to Invest Your Savings? Samsung Insights: Wealth Management. [https:\/\/insights.samsung.com\/2017\/06\/08\/machine-learning-in-finance-is-a-robo-advisor-smart-enough-to-invest-your-savings\/] Accessed November 2018.<\/p>\n

2. Jung, Dominik & Dorner, Verena & Glaser, Florian & Morana, Stefan. (2018). \u201cRobo-Advisory: Digitalization and Automation of Financial Advisory.\u201d Business & Information Systems Engineering. [https:\/\/www.researchgate.net\/publication\/322643071_Robo-Advisory_Digitalization_and_Automation_of_Financial_Advisory] Accessed November 2018.<\/p>\n

3. Citi GPS. (2016). \u201cDigital Disruption: How FinTech is Forcing Banking to a Tipping Point.\u201d [https:\/\/www.nist.gov\/sites\/default\/files\/documents\/2016\/09\/15\/citi_rfi_response.pdf] Accessed November 2018.<\/p>\n

4. Boston Consulting Group. (2009). \u201cWomen Want More in Financial Services.\u201d [http:\/\/image-src.bcg.com\/Images\/BCG_Women_Want_More_in_Financial_Services_Oct_2009_tcm81-125088.pdf] Accessed November 2018.<\/p>\n

5. Ernest & Young. (2017). \u201cWomen and wealth: the case for a customized approach.\u201d [https:\/\/www.ey.com\/Publication\/vwLUAssets\/EY-women-investors\/$FILE\/EY-women-and-wealth.pdf] Accessed November 2018.<\/p>\n

6. Fast Company (2017) \u201cGender-Neutral Investing Is A Fallacy, And Ellevest Knows Why.\u201d [https:\/\/www.fastcompany.com\/40489502\/gender-neutral-investing-is-a-fallacy-and-ellevest-knows-why] Accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

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