Wealth Management Lead Identification: Using Graph Data Science to Find High-Value Prospects 

How Quantexa graph analytics uncovers hidden opportunities in Private Equity ecosystems 

All names, companies, and figures in this article are synthetic data generated for illustration purposes. Any resemblance to real individuals or organizations is coincidental. 

The Problem: Valuable Prospects Hidden in Complex Ownership Structures 

Wealth managers know that relationships win business. The most valuable prospects often sit inside sponsor ecosystems - Private Equity firms, Venture Capital, and similar investment networks. These individuals have substantial wealth tied to portfolio companies but finding them and reaching them through warm introductions is extraordinarily difficult. 

Why? Because ownership structures are deeply complex. 

Consider a typical scenario: A Private Equity firm owns a fund, which owns another fund, which in turn owns a portfolio company. This chain can go five, six, or more layers deep. Within these structures, high-net-worth individuals hold indirect ownership stakes that translate to significant wealth - but are nearly impossible to trace manually. 

 

 Several challenges make this difficult: 

• Sponsor ecosystems are large, complex, and computationally expensive to map out 

• Connecting internal and external data for both people and businesses is difficult 

• Ownership chains run deep - calculating indirect ownership requires sophisticated algorithms 

• Scaling analytics to prioritize focus and automatically identify the highest-value opportunities is hard 

 

Manually tracing these connections is slow, inefficient, and often misses the most valuable indirect relationships entirely. 

The Solution: A Three-Step Graph Data Science Approach 

Our approach follows three key steps, building on Entity Resolution as the data foundation: 

 

1. Knowledge Graph Construction - Structure unified data into a Quantexa Knowledge Graph 

1. Hierarchy Algorithm - Calculate true ownership stakes across all layers 

1. Lead Scoring - Prioritize prospects using graph-derived metrics 

 

Step 1: Building the Knowledge Graph 

After Quantexa Entity Resolution creates a unified view of the different entities in the data, like people and businesses, we structure this data into a Knowledge Graph. The Knowledge Graph maps out relationships across the entire dataset – in our case businesses and individuals and how they connect. 

Think of it as taking all the fragmented data about companies, funds, and people, and turning it into a single, connected picture. Every shareholder relationship, every board seat, every subsidiary - all visible in one place. 

 

• A visualization of theKnowledge Graphcreated from corporate registry and customer data
• Businesses, individuals, places, events and how they connect — across the entire dataset

 

Ownership Ontology: Simplifying for Scale 

To make the solution scalable and target it to our problem, we use Knowledge Graph Ontologies/Perspectives to collapse shareholder and subsidiary relationships into a single "ownership" edge. 

 

This simplification: 

• Reduces computational complexity 

• Allows downstream analytics to focus on what matters - the ownership chain 

• Eliminates the need to traverse every relationship type in the full graph 

The result is a complete map of who owns what across the entire ecosystem. 

Step 2: The Hierarchy Algorithm - Uncovering Hidden Ownership 

Here's where it gets interesting. 

Looking at the data, we see direct shareholder information i.e. Sarah Mitchell shows up as a 8% shareholder in a company called Quantum Shield. But Quantum Shield sits inside a Private Equity ecosystem - owned by funds, which are owned by other funds, which trace back to a major sponsor. 

When we follow the ownership chain all the way through, Sarah's 8% stake connects her to hundreds of millions in value across the portfolio. 

The Hierarchy Algorithm propagates ownership through every layer, answering: "Who really owns what, when you follow the money all the way through?" 

Suddenly, hidden relationships surface: 

• Someone with "just 8%" of one company has indirect exposure worth hundreds of millions 

• A prospect buried four layers deep is connected to companies you already serve 

• Warm referral paths appear that were invisible before 

 

Step 3: Lead Scoring - Prioritizing with Graph Metrics 

Now that we understand the full ownership picture, we can identify and prioritize potential leads using three graph data-driven scoring models: 

 

 

Estimated Net Worth

Formula: Σ (Ownership % × Company Valuation) 

Using the indirect ownership percentages calculated by the hierarchy algorithm, combined with company valuations, we estimate each individual's net worth derived from their portfolio holdings. 

Example: 16% indirect ownership × $3,500M company valuation = $560M estimated net worth 

This provides a baseline understanding of potential wealth.

Relationship Strength

This score measures a prospect's connections to existing customers, considering: 

• Number of shared connections 

• Roles (e.g., co-directors, board members) 

• Recency of relationships 

 

A high relationship strength score means stronger referral paths and higher likelihood of conversion. Prospects connected to multiple existing customers through meaningful business relationships are more likely to respond to warm introductions. 

Network Influence

This centrality metric measures how connected an individual is across the entire Knowledge Graph - not just within one sponsor ecosystem. 

Highly influential individuals: 

• Sit at network hubs connecting multiple businesses 

• Have higher lifetime value 

• Offer greater future referral potential 

The Results: Actionable, Prioritized Leads 

 
Below is a snapshot of the top results - for each lead, we provide: 

• Estimated net worth derived from indirect ownership calculations 

• Network influence score based on graph centrality 

• Relationship strength to existing customers 

• The exact referral path - which existing customers can provide warm introductions 

 

 

 

For our top prospect, Sarah Mitchell, we not only see her high net worth score but also the exact referral path: an existing customer, John Fitzgerald, along with the portfolio company where they're connected (Quantum Shield). This gives the relationship manager everything needed to make an informed, warm approach. 

 

Visualizing the Ownership Flow 

The hierarchy graph visualization shows the complete ownership chain from sponsor to portfolio company to prospect: 

 

This produces a visual hierarchy showing: 

• Pinnacle Horizon Ventures LLC (Sponsor) at the top 

• Fund structures (Pinnacle Climate & Renewal Fund, Pinnacle Digital Frontier Fund) 

• Portfolio companies (Quantum Shield, Synapse AI, Circulus Materials, etc.) 

• Individual shareholders with their ownership percentages 

• Existing customers highlighted for referral identification 

 

Technical Stack 

• Quantexa Knowledge Graph (QKG) - Graph construction and ontology management 

• Legal Hierarchy Algorithm - Indirect ownership propagation 

• PySpark - Distributed processing for scale 

• Graph Centrality Algorithms - Network influence scoring 

 

This approach demonstrates how graph data science transforms complex relational data into actionable business intelligence. By combining Knowledge Graphs, hierarchy algorithm, and graph-based scoring, wealth managers can identify high-value prospects they would otherwise never find - and reach them through warm, trusted relationships.