Q&A with Jeremi Karnell: Optimizing wealth management with data

1 MIN. READ

Jun 28, 2024

Jeremi Karnell, Head of Envestnet Data Solutions, shares his thoughts into how firms are leveraging Envestnet’s data capabilities.

The Envestnet Wealth Management Platform serves as the central access point for our clients, connecting their entire wealth technology ecosystem and adding value through data and intelligence, integrated workflows, wealth solutions, and an efficient advisor/client experience.

How are firms and advisors leveraging Envestnet’s data solutions today within our Wealth Management Platform?

Data and analytics can enhance many different aspects of the wealth management process, but let’s start with four of the most common challenges I see when working with our clients.

  1. Data integration and management: Firms often deal with data from multiple sources such as investment platforms, financial planning tools, and client relationship management systems. Integrating and managing this data cohesively can be difficult.
  2. Data accuracy and consistency: Ensuring data accuracy and consistency is crucial to making informed decisions. Inconsistent or inaccurate data can lead to poor investment decisions and loss of client trust.
  3. Data reporting and visualization: Creating meaningful reports and visualizations from complex data sets can be time-consuming and challenging for advisor.
  4. Personalized client insights: Delivering personalized advice requires deep insights into client data. However, extracting actionable insights from vast amounts of data can be complex and daunting.

To dig in a little on the last point, we recently worked with a broker-dealer to rollout our Insights Engine to their network of independent financial advisors. The goal was to simplify the process of identifying the accounts that were the best fit for their managed account service and equip their advisors with the context and proof points to initiate successful client conversations.

The Insights Engine flagged underperforming portfolios based on the investor’s preferences, current risk, and target risk, and they could prioritize based on risk deltas and asset base. Advisors could then talk to their clients about why they might be better served with a managed account and provide the supporting details on where their current risk and target risk is, how their under- or over-diversified portfolio compares to a well-diversified managed account. The firm saw an increase in their Unified Managed Account (UMA) and Fund Strategist Portfolio (FSP) net flows by over 34% in the first three months.1

What is next on your team’s roadmap and what can advisors look forward to?

We are bringing to market Envestnet Data Solution’s first knowledge graph.

A knowledge graph is a structured representation of real-world entities and their interrelationships, providing a contextual framework to integrate and analyze data from various sources. Basically, it connects diverse pieces of information, revealing how different entities like people, places, and events are related to each other.

Building a knowledge graph of Envestnet’s data involves mapping out financial products and services as distinct entities. These entities include checking accounts, savings accounts, credit cards, loans, wealth management services, insurance policies, options contracts, and futures contracts. Each entity is defined by specific properties. For example, wealth management services might be characterized by their asset allocation strategies, risk profiles, and management fees, while credit cards could be described by their credit limits, reward programs, and interest rates. Our knowledge graph will capture the relationships between these entities, providing a comprehensive view of the financial ecosystem.

Can you give a few examples how what these relationships look like?

Investment relationships can be detailed in several ways:

  • The investor-to-investment relationship maps out who invests in what, including details such as the investment amount, type of account (e.g., retirement account), and transaction history.
  • The fund-to-underlying asset relationship shows the specific stocks, bonds, or other assets held by a mutual fund or ETF, along with their respective weightings.

Building this knowledge graph is a critical step in enhancing Envestnet’s Wealth Management Platform. It enables us to deliver more precise insights, foster better decision-making, and provide a more personalized experience for our clients. By understanding and visualizing the complex web of financial relationships and properties, we can develop innovative features and services that meet the evolving needs of our users.

What are some real time knowledge graph applications?

Our team anticipates that home office / enterprises could use the knowledge graph for:

  • Advisor as Portfolio Manager (APM) Watch list: The graph could help identify a compilation of profiles that have below average performance in APM business relative to peers with high proportion of APM business.
  • Pricing best practices: The graph could identify top tier advisors based on their pricing practices as well as other factors e.g. Performance, Client Stability, etc.
  • Coaching opportunities: The graph could potentially flag advisors with characteristics such as low conversion rate, above average investment policy violations/warnings, or above average practice level outflows.

At the same time, advisors might use the knowledge graph to:

  • Uncover clients based on key business drivers including growth opportunities, client risk and satisfaction
  • Find the closest managed portfolio to self-managed account in case of transitioning advisors to prevent tax liability and similar characteristics
  • Identify business metrics and granular profile attributes where you are an outlier and address the situation as needed

As we get closer to launch, we will share additional applications and examples in our demos and release notes.

How do you see the data opportunity evolving over time?

As data becomes more abundant, Decision Intelligence (DI) is emerging as a crucial trend in the financial services industry. Firms can use DI tools to analyze financial data and client information, providing personalized advice and helping to improve client satisfaction. Envestnet is at the forefront of this, enabling intelligent decision making for firms without requiring them to build new systems or grow data science capabilities.

To learn more about our connected wealth management experience, please visit https://www.envestnet.com/wealth-management/software

Jeremi Karnell

Written by Jeremi Karnell

Head of Envestnet Data Solutions

Jeremi Karnell, Head of Envestnet Data Solutions, leads a team pioneering data-driven insights and business intelligence for Wealth Enterprises, Advisors, RIAs, and Asset Managers. He oversees the Wealth Data Platform, Insights Engine, Manager Analytics, and RIA Analytics. Jeremi co-founded Truelytics, a valuation solution for financial advisors and RIAs, acquired by Envestnet in July 2022.

View all posts by Jeremi Karnell

The information, analysis and opinions expressed herein are for informational purposes only and do not necessarily reflect the views of Envestnet. These views reflect the judgment of the author as of the date of writing and are subject to change at any time without notice. Nothing contained in this piece is intended to constitute legal, tax, accounting, securities, or investment advice, nor an opinion regarding the appropriateness of any investment, nor a solicitation of any type. Potential transactions identified by the Insights Engine are for informational purposes only and are not to be construed as an instruction to take any specific action. Envestnet, Inc. and its subsidiaries and affiliates are not responsible for any decisions or recommendations you may provide to your clients. Past performance is no guarantee of future results. *Results are not indicative of all firms and cases.

 

Potential transactions identified by the Insights Engine or Non-Managed Insights tool are based on concentrated positions, concentrated asset classes, and/or high cash allocations but do not include a fee analysis or other factors that should be taken into account when considering brokerage versus advisory accounts.  Potential transactions identified by the Insights Engine are for informational purposes only and are not to be construed as an instruction to take any specific action. Envestnet, Inc. and its subsidiaries and affiliates are not responsible for any decisions or recommendations you may provide to your clients.

 

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1Calculated via Envestnet Manager Analytics. Review of UMA and FSP Inflows and Outflows from Sep-22 to Aug-23. Performance numbers shown are averages of APM and FSP accounts pulled from Envestnet’s UMP dataset for a given year and risk rating. Performance does not reflect actual trading costs and may not include the impact that material economic and market factors might have had on the adviser´s decision making if the adviser were actually managing client assets. The performance of an actively managed account would likely not mirror these averages, and actual client returns may vary from returns shown. Data through 3/31/24.