AI and ML empower advisors by predicting their clients’ economic behaviour

Vasyl Soloshchuk, Insart: the advisor’s role goes beyond managing money

Smart data analysis can enhance advisors’ communications by giving them intelligent behaviour prediction tools.

The advisor’s role goes beyond managing money; it has shifted to managing lives. About 85% of an advisor’s time is spent explaining, communicating, and engaging clients, rather than rebalancing or making calculations:

“Most people think about automation in the context of rebalancing and trading. But even an advisor who’s doing a fair amount of trading is probably only spending 10-15% of their time on trading and rebalancing. And most of their time is sunk into communicating and engaging with clients.”
John Prendergast, CEO and co-founder of Blueleaf

This is why the automation focus has shifted to building tools able to disengage advisors from micro-conversations and discover factors influencing clients’ economic behaviour.

Fintech platforms have a lot of information about advisor and client behaviours. They store billions of emails, calendar events, notes, unstructured text, and data in the client relationship management (CRM) systems. Using this data and machine learning (ML) algorithms, they can provide valuable insights and provide personalised experiences.

Natural language processing (NLP) is used for teaching computers to extrapolate meaning. By analysing user requests it can categorise them, define what’s being asked for, and suggest a quick solution. This leaves advisors free to lend a hand only when an uncommon issue occurs, with algorithms doing all the rest.

Also, when transfer learning is applied, the platform can provide rich insights even if there still isn’t a lot of data – this method applies knowledge gained from solving one problem to a different but related situation.

Below are several more examples of how artificial intelligence (AI) and ML can improve wealth management while predicting clients’ behaviour, and more.

Defining trends

An essential part of automating communication is trend and sentiment determination. How do you know who might have a problem? Who should be involved in the conversation? What topics should be discussed? Advisors know the answers to these questions intuitively as they constantly monitor trends and changes. However, they can forget some client-specific details that can influence the decision greatly – a marriage, the birth of a child, tragic events, and so on.

The problem is that our brains can’t retain so much information for the entire client database, which leads to opportunities being wasted. ML enables automation of these activities while freeing advisors’ hands (and minds) up to provide an enhanced user experience.

AI helps to identify clients’ sentiment and show stats

Keyphrase extraction

Based on unstructured text analysis, state-of-the-art tools can help advisors identify problems that their clients have. Keyphrase extraction algorithms can detect clients’ interests, behavioural trends, etc. By having this information already withdrawn and structured, the advisors will know what to talk about with this or that client and be able to elaborate a comprehensive action plan.

The system runs automated tagging for all of the client’s text materials. Extracted keyphrases are usually categorized into groups, so they can be automatically inserted into the client’s CRM profile gaps. Specifically, platforms are capable of distinguishing names, locations, financial instruments, events, etc. Once all this data has been structured, they may even suggest several solutions typical of related requests, thereby automating support.

What else can smart keyphrase analysers help advisors with? When integrated with workflows, they may help to optimise and personalise timing. In addition, algorithms can display changes in risk tolerance based on events that are happening to clients and their ambitions. Overall, AI can save huge amounts of effort while opening opportunities for advisors to grow and enhance their service.

Top successful AI use cases

• Redtail provides advisors with three specific actionable feedback buckets: sentiment, keyphrases, and entities or tags, such as specific types of investment accounts. The company has also launched a compliance-approved text messaging service, which enables mobile texting that doesn’t require the client to set up any additional software or apps.
• Behavioral IQ from Advisor Software provides planners with detailed insights into six different behavioural factors influencing clients’ thoughts on risk and decision-making. At its core is a risk-scoring mathematical model that measures a person’s relevant behaviors in order to allow them to make better financial decisions.
• Another new tool, Finworx from Lirio, also uses behavioural finance-based questions to provide advisers with a deep understanding of how their clients or prospects approach decision-making, and how they react to risk. Also, this solution helps organisations motivate the people they serve to achieve better outcomes.
• Studies show that people respond to text messaging 90% more than emails. This is why Wela empowered the communication module of Benjamin with AI-powered push notifications and chatbots to make the user experience unique.

Takeaways

The wealth management industry has faced a shift in the role of the financial advisor. Meanwhile, intelligent data-processing technologies have led to tools that have made this shift much more seamless.

Harvesting behaviour predictions allows advisors to provide great service while utilising reasonable resources.

By Vasyl Soloshchuk, CEO of Insart