Artificial Intelligence (AI) is being tested out in a range of industries, from fast food to agriculture. Even with machine learning technology being used on conveniences, such as personal assistants and self-driving cars, where it is set to make one of the biggest impacts in finance is in private wealth management.
With portfolios of $20 million in invested assets and stocks, private wealth management clients require more attention than the average banking client. The use of AI allows wealth managers to give greater attention to these high-value clients.
One of the benefits of AI is the ability to read big data and remove humans from investment equations, which lowers the risk of errors that can be made. AI systems are able to look over large quantities of stock information, company details, and markets in order to predict positive financial outcomes.
Systems may be put in place to purchase stock when favorable conditions are met. This creates a more accurate solution for private wealth management and customers.
Though customer relations management (CRM) software has made sales easier over the years by collecting customer information from various channels, those systems become tedious and bogged down after enough time.
US cloud computing company Salesforce announced the release of Financial Services Cloud Einstein. Basically, a CRM system with a built-in AI that projects a clean overview of a client’s wealth and features which allows for new business opportunities. The software can take this a step further by alerting sales reps to the last time a lead was contacted, if a competitor was mentioned in an email, or even mapping out the customer’s portfolio.
With the rise in chatbots, many companies have found a way to include these messaging programs in financial services. In the US alone, there are over 100 financial companies using AI as part of their offerings. Of those, over 12 are using chatbots for personal assistance.
At VentureBeat, one author detailed how he was able to build a financial chatbot in six months. This included creating the system, the software he used, and how he changed focus in order to gain more customers by partnering with a bank in New York City.
Regardless of how much information is captured and sorted though, there is still the need for a human touch when it comes to investing.
In the startup space, many Angel investors, those who invest in early-stage companies, don’t necessarily look at a startup’s financials, but rather the business model and what kind of people the founders are. These types of human variables are impossible for an AI to work through, regardless of how much data they are given.
Of course, any system released too early or left unchecked can lead to a few problems. In 2016, Microsoft launched Twitter chatbot Tay as an experiment in conversational understanding. Unfortunately, Tay didn’t function as intended and within 24 hours was turned into a racist misogynist.
While Microsoft incorporated user cleaned up data from Twitter to create the basis for Tay, once the system launched it appeared as though all of the filters were lost. This resulted in the system repeating a range of unsavory tweets and ones that it wasn’t asked to.
As Tay shows, there is a need for human facilitation with AI and chatbots in general. Even though a banking chatbot may not call customers a range of racial slurs, it is possible to give them the incorrect information when asked crucial banking questions. The same can go for automatic investing AI, which could purchase the wrong stock if its data is not checked. This would impact private wealth management in a negative way.
AI may be improving the financial sector in a big way, it still has a long way to go, but strides are being made. The Vector Institute in Canada has managed to raise over $150 million for research into AI with the likes of Microsoft, Apple, and Uber also investing into the technology.
In order to help your own financial company grow, take a look at Clickatell’s Touch. It enables call center agents to deal with multiple conversations and customer issues at once. The system’s bot-driven interactions are able to learn and therefore help the customer more efficiently.