Integration with Postgres Database for ML scoring
For a large wealth management company
CLIENT & PROBLEM STATEMENT
- A large Indian wealth management company seeks automation of the ML-driven sales enhancement strategies.
- Requirement to utilise on-premise Postgres data for predictive sales analytics.
APPROACH
- Integrated with client’s on-premise PostgreSQL database to extract comprehensive sales and client data.
- Engineered a sophisticated Machine Learning (ML) pipeline for efficient batch processing of data.
- Implemented Clientrator, a proprietary tool, to fine-tune ML outputs based on client segmentation and relationship history.
SOLUTION & OUTPUT
- Batch ML process with Clientrator provides hyper-personalised fund recommendations and client retention strategies.
- Sales teams armed with actionable ML insights, driving targeted engagement and increased Assets Under Manage
- The solution successfully processed 48 millions of historical data, providing actionable insights that improved client retention strategies and enhanced fund recommendations.