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.
  • Applied advanced feature engineering to uncover insights from client portfolios, fund historical performance, and macroeconomic indicators.
  • Leveraged ensemble ML models (e.g., Gradient Boosting, Neural Networks) for accurate client churn prediction and personalised fund suggestions.
  • 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 Management (AUM).
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