Traditional investment advisors manage their clients and prospects with traditional tools such as those found in “Seven Habits of Highly Effective People” (Stephen Covey, 1989). They often focus solely on personal relationships or a simple metric such as assets under management. Little is paid to utilizing existing client information stored in the Investment Policy Statement and demographics to forecast client profitability. Wealth Analytics AI aims to target U.S. based high net worth wealth managers with $10B to $100B in assets under management (AUM).
Wealth Analytics AI proposes to build predictive models to improve client management and the profitability of the book of business. Predictive analytics can help:
- Reduce client churn – Identify clients that need of attention, and those that represent the future of an advisors portfolio of clients.
- Improve client acquisition rates – Identify the best prospects, given limited time, high acquisition costs in a competitive landscape.
- Help optimize internal team performance – Identify traits of team network dynamics, match the best teams with clients that go beyond simple traditional metrics such as geography.
Example 1 – Improve Client Retention by 1%:
$10B AUM Wealth Manager with 2,000 clients (Avg. acct $5M), with a net advisor fee of 1%
A predictive model that can help reduce client churn by 1% of churn per year can avoid a loss of $100M in assets:
Improvement of $1M per year in fees + $200k in CAC
Example 2 – Improve Client Acquisition Rates by 5%:
Same $10B AUM firm, 10 salespersons, each salesperson has 40 prospects per year
Improving win rate from 25% to 30% is worth $1M per year
Example 3 – Measure and Improve Peak Performance:
Same $10B AUM firm, 20 Portfolio Managers, 10 Salespersons
Team Analytics – Measure quantitative metrics on team network dynamics and combinations of relationships and how they quantitatively affect the overall firms portfolio of clients, suggest relationships structure, teams and client relationships that go beyond geographical location.
- Strong interaction of teams and networks
- Measure team performance
- Help build better teams
Wealth Analytics AI Team:
Geoffrey Pazzanese – Founder / CEO / New York
- Investment professional with 20 years of experience in wealth management as fund manager and RIA
- Expert in predictive model construction for investing
- Series 7 (General Securities Rep.) and Series 66 (Investment Advistor Representative)
- HBAP, MBA, BS Physics
Jeff Olfert – Founder / CTO / Portland
- Software Engineer with 20 years of experience of designing software systems and platforms
- Expert in agile methods for software modeling, machine learning, and AI
- Client focused results
- HBAP, BSEE Electrical & Computer Engineering
Better Models Faster
Winning models ensemble methods, logistic regression, random forest, et al. using deep learning, machine learning.
Combined leadership and domain expertise in both technology and wealth management. Agile methods to continually produce winning predictive models.
Your data, your control. We build predictive models leveraging existing data in current database and archived historical data. Using latest U.S. based storage from Amazon RDS distributed for resiliency.
2020 & 2021 – Pilot Program – White Label Models
Work with mid-sized high net worth (HNW) money manager to explore data, build predictive models across key areas, deliver model outputs and signals. Measure performance and client success.
2022 – Expand Client Base – Product Development
Project work with two to three mid-sized wealth managers on predictive models for HNW clients and internal teams, with delivery across proprietary platform.
2023 – Platform – Predictive Analytics CRM Intelligence
Multiple wealth management firms and offices delivering model results across proprietary platform. There are over 400,000 financial advisors in N. America.