Morgan Stanley's Next Best Action AI boosts client engagement by 30% across 15,000+ advisors
Morgan Stanley deployed Machine Learning & Predictive Analytics for Personalized Financial Insights in Wealth & Private. As reported by aiinx.ai: 30% client engagement increase.
Source-reported figures — cited source: aiinx.ai
What Morgan Stanley was trying to fix
Morgan Stanley's 15,000+ financial advisors faced time-intensive manual research to uncover the right investment recommendations, requiring them to comb through large volumes of research, market data, and client information. Scalability limits meant advisors struggled to serve growing books of business, and recommendations were inconsistent depending on individual advisor capacity. There was also growing client demand for more tailored, data-backed advice in an increasingly competitive wealth management landscape.
What Morgan Stanley deployed
Morgan Stanley deployed 'Next Best Action' (NBA), an internal AI engine that analyses client preferences, portfolio history and behaviours, real-time market conditions, proprietary research, and product suitability and compliance filters. The system generates personalised recommended actions and messages advisors can send to clients, including rebalancing suggestions, timely product recommendations tied to life events or market trends, and follow-up prompts after portfolio underperformance. By mid-2022, over 90% of Morgan Stanley advisors were actively using NBA.
Results
The system delivered a 30% increase in client engagement with investment proposals. Advisor productivity improved, enabling more clients to be served without increasing headcount. Response times for client needs shortened with proactive, timely insights, and consistency and compliance in recommendations were enhanced. Junior advisors were empowered with the same insight quality as seasoned professionals.
Key Takeaways
- AI augmentation rather than replacement creates a force multiplier effect: 90%+ advisor adoption shows the tool enhanced rather than disrupted existing workflows.
- Combining real-time market data with client behavioural data enables hyper-personalised outreach at scale, addressing both consistency and relevance challenges.
- Embedding compliance filters directly into the AI recommendation layer reduces regulatory risk while accelerating advisor outreach.
Evidence for Morgan Stanley's Personalized Financial Insights deployment
- Reported outcome metrics
- 2 cited below
- Cited source
- aiinx.ai
- Last updated
- Source link checked
Explore Related
Details
- Industry
- Wealth & Private
- Use Case
- Personalized Financial Insights
- AI Technology
- Machine Learning & Predictive Analytics
- Company Size
- Enterprise
- Company
- Morgan Stanley
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