by
Barbara Stewart*
However, while women are the target customers, they are still underserved by financial advisers. Most financial services firms have optimised 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 behaviour of women 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 analyse client behaviour and use the data to deliver individualised advice based on their investing, saving, and spending habits”.
Machine learning allows us to crunch data and see behaviour patterns.
Deloitte recently released their Technology, Media and Telecommunications Predictions for 2018, including one of the key forecasts “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] behaviour is. These are all elements we could only dream about a couple of years ago.”
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 idealised 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 delve into the data about female investors and then capitalise on their evolving investment behaviour patterns.
Anna Svahn, manager of Feminvest in Sweden and an author and investor, offers a case in point: “In January this year, I … took over Feminvest, a female investor network with about 15,000 members.
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.”
What’s the bottom line?
Globally we are in the midst of a radical shift in socialisation. We are seeing explosive growth in the number of social trading platforms and social media communities directed at women.
As I pointed out 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.”
Female-focused machine learning, powered by new hardware and software, will be a key trend for 2018 and beyond.
*Researcher and author on the issue of women and finance
*First published in Cityam.com
By: N. Peter Kramer
