Organic growth has long been a challenge for wealth companies, which appears to be growing in difficulty. The key to organic growth is understanding the full picture of client wealth and held-away assets. However, this can be extremely difficult, requiring continuous innovation and customized solutions across a wide range of financial products and services to capture a larger share of the client's asset portfolio. Enter data science—a transformative approach that uses artificial intelligence and machine learning to uncover growth opportunities.

Key applications of data science

Data science offers multiple avenues to drive organic growth:

1. Acquiring new customers: AI helps identify, prioritize and convert prospects into customers by analyzing demographic and financial characteristics.
2. Increasing existing client assets: AI envisages the opportunity for existing clients to consolidate their assets with the firm, thereby increasing assets under management.
3. Customer and asset retention: AI shows potential risk behaviors that match customers 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 such as Random Forest, enables wealth management firms to predict customer behaviors and identify high-value opportunities. For example, AI can identify new prospects that fit a firm's ideal customer profile, predict the value to the firm of acquiring those prospects, and explain why each prospect is a good fit and likely to convert . This provides firms and their advisors with highly targeted growth prospects.

Supervised AI algorithms can also predict the best opportunities to grow a firm's existing customer base. By analyzing client demographics, finances and asset flows over time, AI can predict which clients are most likely to consolidate their external assets in the next three months, which tells advisors who to target are addressed and when.

One of the most important aspects of all these models is the feedback loop – incorporating engagement data and results back into the model to improve accuracy over time. In the acquisition example, this means collecting data on which potential advisors reached out, who engaged, who accepted appointments, and who ultimately converted. This feedback data helps adjust 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 used to learn patterns from client data and predict the next best opportunity, because AI models are constantly retrained through a feedback loop created by advisor activities and data. monthly asset flow. This process helps advisors identify and prioritize clients who are most likely to consolidate their retained assets, thereby driving net new assets and giving firms a competitive advantage.

Without AI, advisors often struggle to identify high-ROI opportunities, wasting time on less promising clients and administrative tasks. AI-driven insights help advisors focus on the right customers at the right time, making their field efforts more effective.

CONCLUSION

Data science and AI are revolutionizing the wealth management industry by providing accurate insights that drive organic growth. By leveraging these technologies, firms can better understand customer 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 sustainable growth and competitive advantage.

Laura Kimble is Head of Data Science at TIFIN AG