Organic growth has long been a challenge for wealth enterprises, which only seems to be increasing in difficulty. Key to organic growth is understanding the full picture of client wealth and held-away assets. However, that can be notoriously difficult, necessitating continuous innovation and personalized solutions across a wide array of financial products and services to capture a larger wallet share of client assets. Enter data science—a transformative approach that leverages artificial intelligence and machine learning to uncover growth opportunities.
Key Applications of Data Science
Data science offers multiple avenues to drive organic growth:
- Acquisition of New Clients: AI helps identify, prioritize and convert prospects into clients by analyzing demographic and financial characteristics.
- Growth of Existing Client Assets: AI predicts the likelihood of existing clients consolidating their assets with the firm, thereby increasing assets under management.
- Retention of Clients and Assets: AI flags potential risk behaviors that align with clients who have previously reduced or divested their assets, enabling proactive engagement to mitigate outflows.
The Role of Data Science in Wealth Management
Data science, particularly through the use of supervised AI algorithms like Random Forest, enables wealth management firms to predict client behaviors and identify high-value opportunities. For example, AI can identify new prospects that fit a firm’s ideal client profile, predict the value to the firm if they were to acquire those prospects, and explain why each prospect is a good fit and may be likely to convert. This equips firms and their advisors with highly targeted growth prospects.
Supervised AI algorithms can also predict the best opportunities to grow a firm’s existing client base. By analyzing client demographics, financials and asset flows over time, AI can predict the clients who are most likely to consolidate their outside assets in the next three months, which tells advisors who to reach out to and when.
One of the most important aspects of all of these models is the feedback loop – incorporating engagement data and outcomes back into the model to improve accuracy over time. In the acquisition example, this means collecting data on which prospects advisors reached out to, who engaged, who accepted meetings and who ultimately converted. This feedback data helps fine-tune and improve the model to learn how advisors and prospects are behaving in the real world.
Supervised AI and the Asset Consolidator Model
Specifically, AI algorithms can be leveraged to learn patterns from client data and predict the next best opportunity because AI models continually retrain through a feedback loop generated by advisor activities and monthly asset flow data. This process helps advisors identify and prioritize clients who are most likely to consolidate their held-away assets, thereby driving net new assets and giving firms a competitive edge.
Without AI, advisors often struggle to pinpoint high-ROI opportunities, wasting time on less promising clients and administrative tasks. AI-driven insights help advisors focus on the right clients at the right time, making their outreach efforts more effective.
Conclusion
Data science and AI are revolutionizing the wealth management industry by providing precise insights that drive organic growth. By leveraging these technologies, firms can better understand client behaviors, identify high-value opportunities and optimize their outreach efforts. As the industry continues to evolve, those who adopt AI and data science will be well-positioned to achieve sustained growth and competitive advantage.
Laura Kimble is Head of Data Science at TIFIN AG
