A Human and Machine Approach to Wealth Management


Chris Kovel
Head of Wealth Management Analytics, Intelligence & Data Technology
Morgan Stanley

Chis Kovel Bio

Chris works in Morgan Stanley’s IT department, helping to develop and enhance such digital technologies as data, analytics, artificial intelligence, machine learning, call center, intelligent agents and CRM. 

He holds two patents for “transaction system for employee stock options and other compensation programs,” a system for selling employee-issued internal options with time value and “company and contact information system and method,” a system for delivering company-related information through signals such as color.

https://youtu.be/XCpOZCIAs0k
Watch Chris Kovel’s full presentation here

Introduction

Your next wealth advisor may be half-human, half-machine. Morgan Stanley financial advisors are leveraging machine learning to gain insights for their clients. A human + machine approach helps enhance client relationships by providing unique opportunities for growing wealth. 

“The greater the degree of organizational focus on people heading AI and AI helping people, the greater the value achieved.”

H. James Wilson — Managing Director, Accenture

AI trends in marketing

Marketers are experimenting with AI in a number of ways:

  • Real time next-best offers
  • Predictive journeys
  • Improved customer segmentation
  • Automated social and messenger app interactions
  • Personalized channel experiences
  • Personalized overall customer journeys
  • Dynamic web pages and websites
  • Offline/online data experience facilitation
  • Programmatic advertising and media buying. 

The following marketing technology is going to increase 257 percent in the next two years: average use, planned use, and projected growth of AI use cases. 

Machine-learned marketing journeys are focused on getting prospects to become customers. 

How: offering products and services based on the prospect’s data. Advisors continue to offer more products and services relevant to the prospect until they decide to open an account. 

Experts + algorithms + intuition = differentiated insights

How this is achieved: 

  • Create models that prioritize and learn
  • Combine financial advisors’ experience, intuition and empathy
  • Create the best possible decisions are then created 

Morgan Stanley’s “Next-Best” Action:

Identify the most impactful and relevant ideas, customized to the goals and preferences of each client, and then optimize the engagement of these opportunities across the most appropriate channels. 

  • Examples: service alerts, investment ideas, life events (new parents, college tuition, house purchase, retirement, birthdays) via email (this is the primary channel), phone, text, social media and mobile. 
  • Which investment products should the prospective customer consider? Advisors are looking out for not just the clients’ financial wellbeing but also their overall wellbeing. 

Explaining Explainable AI (XAI)

  • Build models that express interpretability on top of inputs of the AI model. 
  • Reversed time attention model (RETAIN): developed to help doctors understand why a model predicts patients risking heart failure. 
  • Models that change the entire form of the AI. 
  • Scoring algorithms that inject noise with local interpretable model-agnostic explanations (LIME). 

Take one client and break them down into a thousand little pieces. Create a knowledge graph of them. We learn what our clients become. 

How: Genomic Machine Learning Models

  • Derive a person’s profile 
  • Use machine learning to explain what a person is interested in
  • Apply to business ideas and marry it with producer-consumer relationships, aka “collaborative filtering.”

How to share this AI information: Through key stakeholders: business, data scientists and analysts, and technology.

Challenges to the democratization of AI: 

  • Data accessibility
  • Tools and technologies 
  • Collaboration 
  • Production deployment

Bottom line: the difference between empathy and knowledge

Financial advisors have empathy and they know how to bring empathy to their decisions — but our machines bring the knowledge. There is no empathy built into these AI models — only knowledge. The human component brings the empathy; that is, the financial advisor’s relationship with the clients. A human being managing the customer relationship is still ultimately very important. There is a real core value in providing human advice at all times, even in deciphering what the machine is saying.

“Your next financial adviser might be a centaur — not half-human, half horse, but half-human, half-machine.”

Jason Zweig — The Wall Street Journal

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