For many advisors and other financial industry professionals, conferences and tradeshows quickly fill the calendar as the year progresses. These events have become as much an exhibition of human ingenuity and technological advances as an informative networking platform. Financial technology (FinTech) has never been hotter than it is right now, and its momentum will only continue to grow.
Tradeshows and conferences give us the opportunity to learn from financial experts, and we are passionate about sharing our technology innovations with the industry. From October 12 to 14, Advicent took part in Mortgage Week 2016 in Amsterdam. Advicent CEO Phil Cunningham was involved in various sessions during Mortgage Week and was able to connect with industry professionals in our Netherlands market.
Beginning the big data discussion
Phil took part in an on-stage discussion with Nela Richardson of Redfin, Ewout van Jarwaarde of McKinsey & Company, and Isold Heemstra of ING-DiBa. The discussion began talking about how banks need FinTech, but also about how FinTech needs banks. The question was then posed, “Is big data the new gold?” Phil stated that he believes data is the new gold and claimed, “It's going that way. Data will eat software and software is eating classic advice.”
Phil also told the audience to “use big data for making the customer better and not just stare at big data.” At the end, the moderators of the day asked the discussion participants what is different when you compare the world as it is now with the one when you bought your last house. Phil said, “Buying a house is different now then 12 years ago. We live in a digital world and we think different, but the human still is necessary.” By this Phil means that while digital is changing these processes, the emotion and expertise of a human is still necessary in making these financial decisions.
The smart use of big data for customer activation and robo-advice
Phil also presented on the smart use of big data for customer activation and robo-advice. Phil began by walking the audience through various parts of his LinkedIn profile and showcasing the information housed there. When most people talk about big data, they are referring to the combination of this type of basic information about people, making that information anonymous, aggregating that data in bulk, and analyzing the information to predict a certain situation for another person with similar properties. However, this is not big data.
When the previously mentioned data is combined with information about a person from the government, their grocery shopping habits, social media profiles, and what they read, that is big data. A good example of the use of big data is Mount Sinai. A team led by Jeffrey Hammerbacher has been working on analyzing data from medical files and the output of medical monitoring systems, such as heart monitors and oxygen saturation monitors, to recognize patters that will enable them to predict the success of certain treatments and remain one step ahead of medical events by finding ways to recognize that a heart attack is about to happen.
They want to offer medication and treatment to prevent such events, rather than have the heart attack take place and treat reactively. In February, the hospital published two open source big data applications to research disease progression in order to find patterns in patient or patient behavior characteristics, and that is what big data is all about. Using truly BIG data by combining various large sources of information with the objective to predict, foresee, prevent, or improve a future outcome.
Three elements are very important when we want to make good use of big data:
- Relevancy: The data must be relevant to predict, prevent, foresee, or improve. So we need to have an in-depth knowledge of the matter at hand.
- Accuracy: If you put garbage data in, you get garbage data out.
- Goal definition: You need to clearly define what you want to achieve just like the doctors at Mount Sinai were very clear on their objectives – saving lives by preventing medical issues.
After explaining big data and why it is important, Phil moved to customer activation. You can divide customers into two different categories, retention and resales. Both of the audiences have different needs to engage and they have different characteristics for data to be relevant, accurate, and to specify goals.
The third element of Phil’s presentation was robo-advice. This is also a term widely use, but seldom in the same context. In the U.S., the term is often used for something that could be best described as “automated product selection.” Investment companies often utilize this model of product selection based on a few parameters such as total net worth, age, some client profiling questions with regards to risk tolerance, and objective horizons.
Then there is the automated complex product advice that goes a little beyond that, including a wider variety of products or including more suppliers or product characteristics than asset mix or investment type. This type of robo-advice is generally still supported by generic guidelines based on client or product parameters. Those guidelines are defined by advisor-driven common sense.
Finally, there is the automated financial planning or automated holistic financial advice – the broad type of advice that so far has not been successfully brought to the market place, just the slimmed down category. This is the exact type of advice that could be given to a client through leveraging big data that is specific across a number of different data points.
Earlier this month at Emere, Tim Leberecht of Leberecht and partners from San Fransisco emphasized that elements machines are still lacking are:
Character: Ethos and moral integrity
Acumen: Quick judgment based on intuition
Spirit: Imagination, meaning, and hope
Heart: Passion and compassion
What we see—as Phil outlined—is that the more complex and impactful the advice is, the more consumers feel the need to bring in human expertise and emotion when making financial decisions. Someone can use a medical app to suggest prevention of neck injuries from working at a computer too long. However, when it comes to deciding on whether or not to have open heart surgery, people want personal attention from a physician. These patients feel even more reassured if that specialist is using statistics and advanced professional software to assist in their recommendations.
What does this all mean for financial services?
It is imperative for financial industry professionals to embrace both technology and big data in their client relationships to provide more accurate, relevant recommendations. An online portal offering a more holistic view is an excellent way to support data gathering, provided it is offered in a manner that supports an emotional connection between client and corporation.
Goal definition is also key — do not simply use big data for the sake of using big data. Considering the limited availability of good data, the less you need to look for, the better. Advicent believes that firms should ultimately focus on advisor-supported automated advice to ensure the most holistic advice possible.
Click here to learn more about how Advicent financial planning software can help advisors provide holistic financial advice.