Last month, Softlab360’s CEO, Henry Zelikovsky, hosted a dynamic webinar, “Leveraging Machine Learning for Client Insights and Competitive Advantage,” alongside John O’Connell, founder and CEO of The Oasis Group. The discussion explored how forward-thinking wealth management firms are preparing for and implementing machine learning to drive real business results. Here, we spotlight some of Henry Zelikovsky’s most compelling insights from the session.
The Flexibility of Data Lakes in Modern Wealth Management
Henry Zelikovsky emphasized the transformative role data lakes play in the wealth management sector. Unlike traditional data warehouses, which require highly structured data, data lakes allow firms to ingest and store data in its native format, whether structured or unstructured. This flexibility is crucial for machine learning applications, as it enables firms to experiment with diverse data types and sources without upfront constraints.
Practical Steps for Building Data Lake Infrastructure
When asked about the first steps for wealth management firms looking to build a data lake, Zelikovsky offered a pragmatic roadmap:
- Start with Core Data: Begin by integrating portfolio and CRM data, which are central to wealth management operations.
- Consider Data Granularity and History: Assess the size, type, and historical depth of your data to determine what’s most valuable for analysis.
- Experiment and Iterate: Zelikovsky recommends a proof-of-concept approach: experiment with different leaning methods on available data to see what insights can be derived, then refine your data lake and refine your approach accordingly.
He also highlighted the importance of data ownership and data possession, encouraging firms to maintain control over their data rather than leaving it siloed within third-party platforms.
Machine Learning: From Classification to Actionable Insights
Zelikovsky discussed how machine-learning algorithms, such as classification and clustering, can be applied to data lakes to unlock valuable client insights.
These techniques help firms:
- Segment clients by demographics, behaviors, or investment preferences
- Predict client life events within the context of financial stability and maturity at event occurences
- Predict client attrition
- Identify cross-selling opportunities and forecast portfolio performance
He stressed that not all data is “machine-learnable,” underscoring the need to filter out statistical noise and focus on data that delivers consistent, actionable results.
Visualizing and Acting on Data-Driven Insights
One of Zelikovsky’s most practical points was the value of visualization. By applying machine learning to flexible data lakes, firms can generate intuitive dashboards that make complex insights accessible.
This empowers advisors to:
- See patterns and trends they might have missed
- Challenge their assumptions and strategies
- Make more timely, informed decisions for their clients
Final Takeaway
Henry Zelikovsky’s insights make it clear: For wealth management firms, embracing data lakes and machine learning isn’t just about technology, it’s about gaining the agility to adapt, experiment, and deliver better outcomes for clients. If you missed the webinar, we encourage you to review the recording via the button below or reach out for more information on how these strategies can be put into practice.
Interested in learning more? Let us know which aspect of Henry’s approach you’d like us to explore in future posts!
