Investment Recommendation Engines: Using ML to Automate Investment Advice
Advisors are under significant pressure to lower costs in the face of an ever-tightening regulatory framework while delivering investment advice services across the entire client book. Many financial institutions are currently exploring automated investment systems to ensure standardized CM compliance across all portfolios, reduce the time it takes for advisors to identify portfolios with optimization potential, and to easily simulate the impact of recommended investments to clients.
- Why the traditional investment advice model doesn't work in a post-pandemic world
- Obstacles hindering investment advice automation and how financial institutions are overcoming them
- How investment recommendation engines completely change the traditional investment advice workflow
- In-depth case studies of US and EMEA financial institutions and their utilization of ML to automate investment advice
- Deployment approach and vendors to consider
- Cautionary measures when deploying ML models