Machine Learning: Changing the Game for Women in 2018

How can we use machine learning to better understand our clients? Specifically, our new target customers — women?

Machine learning helps financial institutions to better serve women customers.

Global brands are investing more and more in social media and advanced data analytics. Companies know that women are becoming more prominent as wealth holders, are directing more wealth investing than before, and are often their largest consumers.

Women are the target customers, yet they are still underserved. Most financial services firms have optimized themselves to communicate with and serve male as opposed to female customers. And as my research shows, women think and communicate about investments differently.

The financial industry needs to understand the value preferences and investing behavior of women in order to develop the best advice for how these clients can allocate their resources and values through traditional equity market or alternative investments.

In “Fintech: Revolutionizing Wealth Management,” Marguerita Cheng wrote, “machine learning and other types of AI [artificial intelligence] technology can analyze client behavior and use the data to deliver individualized advice based on their investing, saving, and spending habits.”

I have found that women prefer to invest in the causes and concerns that matter to them. They seek out those securities that best reflect their core values about gender equality, diversity, the environment, and the developing world.

Machine learning will make this information easier to access. Conducting the research on specific investment products will soon take minutes instead of days, and it will be as easy as “point and click” to start investing in a cause.

Machine learning allows us to crunch data and see behavior patterns.

Deloitte recently released their Technology, Media and Telecommunications Predictions for 2018. One of the key forecasts is “Machine Learning: Things Are Getting Intense.”

According to Duncan Stewart, director of Tech Research for Deloitte Canada and author of the report, there are five factors powering a tipping point for machine learning: “Chip improvements, automating data science, reducing the need for training data, explaining the results of machine learning, and better deploying local machine learning.”

Stewart noted:

“These improvements will double the intensity with which enterprises are using machine learning by the end of 2018, and they promise over the long term to make it a fully mainstream technology, one that will enable new applications across industries where companies have limited talent, infrastructure or data to train the models.”

Jon Suarez-Davis, CMO and CSO of the data management platform Krux, said that:

“Machine learning can crunch data quickly, which marks a major shift from marketers combing through spreadsheets to unlock their own insights.

“Marketing is an art and a science. The art is about connecting with humans. The science is spinning up all these insights we could never do on our own and allowing us to ask smarter questions and see these patterns — and now I can activate all these events and start to predict what [consumer] behavior is. These are all elements we could only dream about a couple of years ago.”

Nishant Kumar, in “How AI Will Invade Every Corner of Wall Street,” discussed the potential for faster and more accurate forecasting of metrics like sales data. He wrote:

“The Boston-based firm [Acadian] is investing in AI and big data to better forecast metrics, such as sales, that are key to a company’s performance. If Acadian could wager on sales data before it’s publicly released, the firm would gain an edge.”

Kumar quoted Wes Chan, director of stock selection research at Acadian: “You could use machine learning to get the metric earlier, faster and more accurately. . . . If it works, that’s pretty significant.”

What about bias in data? Will machine learning capture only the stereotypical data about women and investing?

In “Machines Taught by Photos Learn a Sexist View of Women,” Eric Horvitz, director of Microsoft Research, discusses biases in data, pointing out that, “Away from computers, books and other educational materials for children often are tweaked to show an idealized world, with equal numbers of men and women construction workers, for example.”

As Horvitz says “It’s a really important question — when should we change reality to make our systems perform in an aspirational way?”

According to Stewart:

“As banks and wealth firms start using machine learning for better customer insights, they will need to ‘train’ their models on historical data. That legacy data is likely to be dominated by male investors, and any biases in that data set will not only be reflected in the new AI models but may even be exaggerated by the training process. This will lead to the wrong answers when women start representing 50% or more of new business.

“The solution will be to run separate machine learning training on female-only data sets. This will be harder than just using all data from men and women, and it could be slower. But the algorithms that result are almost guaranteed to offer better insights about female customers.”

What are the trends in how women will invest in 2018?

Machine learning will allow us to crunch the data about female investors and then capitalize on their evolving investment behavior patterns.

Anna Svahn, manager of Feminvest in Sweden and an author and investor, is a case in point:

“I started the Economista network on Facebook with Isabella Löwengrip a year and a half ago and we now have 87,000 members. This is the largest financial social community in Europe. In January this year, I also took over Feminvest, a female investor network with about 15,000 members. In Economista, the members discuss both private economics and investing on a basic level and Feminvest is for those who are more experienced investors.

“We will launch a new fund this spring in collaboration with Arabesque Partners. Through machine learning and big data, Arabesque S-Ray™ systematically combines over 200 environmental, social, and governance (ESG) metrics with news signals from over 50,000 sources across 15 languages. Rather than deciding ourselves on the name and focus of the fund, we will show our members which factors are available and we will ask them to vote. If it turns out that gender equality is the most popular factor, we will tweak the fund accordingly.

“Companies want to advertise on the Feminvest platform so that they have access to female investors. They can buy space in our newspapers or on our podcasts and blog. When it comes to marketing to women, investing and networking walk hand in hand. Customer insights drive progress so the faster we can have access to this data (via new technologies such as machine learning) the faster we reach world domination.”

What’s the bottom line on machine learning for female investors?

Finance continues to evolve at a rapid pace. Globally we are in the midst of a radical shift in socialization. We are seeing explosive growth in the number of social trading platforms and social media communities directed at women.

As I pointed out earlier this year, in “Point of No Return: Two Factors Shaping Women and Investing”:

“The world is now one giant investment club thanks to all the new apps and platforms available to investors. Digital investing has opened up the floodgates, and we are on the cusp of a global social movement for women investors. This will have major implications for both the makeup and activity of the stock market.”

Technologies that accelerate our ability to understand women’s investment behaviors are of great interest to all financial institutions.

Female-focused machine learning, powered by new hardware and software, will be a key trend for 2018 and beyond.

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All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.

Image credit: ©Getty Images/jpa1999