Enhancing Investor Risk Profiling with Machine Learning for a Leading US Investment Management Firm
Overview.
A leading US investment management organization aimed to improve risk profiling of high-net-worth individuals (HNI) clients. Coforge implemented a machine learning solution using decision trees, XGBoost, and random forests to enhance predictions and recommendations. This solution resulted in better understanding of customer behavior, improved investment suggestions, and significantly increased the match rate of recommendations.
The client faced significant challenges in risk profiling and managing portfolios for HNI customers:
Critical Role of Wealth Managers: Wealth managers needed to effectively interview clients and manage portfolios.
Risk Profiling: Required accurate categorization of HNI customers into five risk categories: Conservative, Aggressive, Moderately Conservative, Balanced, and Moderately Aggressive..
Our Solution.
Coforge implemented a comprehensive machine learning solution:
Data Preparation: Conducted data cleaning and handling of null values, crucial due to the question-and-answer nature of the data.
Model Selection: Used multiple individual models and misclassification cost analysis to select the best predictions. Models included decision trees, XGBoost, and random forests.
Evaluation Metrics: Applied multiple evaluation metrics to retrain and improve the overall system.
Key Highlights:
Coforge's innovative solution delivered significant value to the client's operations:
Enhanced Customer Understanding: Improved understanding of customer and investment behavior.
Better Risk Profiling: Provided better advice on risk profiling and investment suggestions.
Improved Recommendations: Increased the recommendations match rate by 22% in the first implementation, with a target of 70%.
Effective Workload Management: Streamlined workload management for wealth managers.
The Impact.
Recommendations Match Rate: Improved from 1.5% to 23%.
