Customer Risk Profiling System for Life Insurance Provider
This case study examines the implementation of a Machine Learning (ML) based Customer Risk Profiling System for a leading life insurance provider in India.
IDFC Bank
Client
Banking, Financial Services and Insurance
Industry
Data Platform
Services
Improved Customer Insights
Key Result
The Challenge
Data Silos
Inability to integrate and analyze data from various sources
Limited Risk Assessment
Difficulty in calculating and categorizing customer risk profiles
Inefficient Product Recommendations
Inability to suggest relevant products based on individual risk profiles
Unsuccessful Marketing Campaigns
Difficulty in targeting campaigns based on customer segmentation
Our Solution
Data Integration
Collected and integrated data from various sources into a central repository.
Machine Learning Model Development
Built an ML model trained on EMR to analyze the integrated data and generate individual risk profiles.
Risk Segmentation
Segmented customers into high, medium, and low-risk categories based on their risk scores.
Business Impact
Improved Customer Insights
The system provided a comprehensive understanding of individual customer profiles, enabling better decision-making.
Enhanced Risk Assessment
The ML model accurately categorized customers into risk segments, improving risk management and fraud detection.
Personalized Product Recommendations
The risk profiles allowed for personalized product recommendations, improving customer satisfaction and sales conversion rates.
Targeted Marketing Campaigns
The customer segmentation enabled more effective marketing campaigns with precise targeting and improved ROI.
Operational Efficiency
The automated system streamlined data analysis and risk assessment processes, reducing operational costs.