BluePi helped in Cloud performance for a fleet management company!
11 December 2023
How BluePi Built an End-To-End Video Platform On Cloud
11 December 2023
Customer Risk Profiling System for Life Insurance Provider
Executive Summary/Introduction:
This case study examines the implementation of a Machine Learning (ML) based Customer Risk Profiling System for a leading life insurance provider in India. The organization faced challenges in collating data from multiple sources to gain a 360-degree view of customers and assess their risk averseness. This hampered their ability to offer relevant products, detect potential frauds, and optimize marketing campaigns.
Background:
The life insurance provider, like many in the BFSI sector, struggled with integrating data from various sources, including demographics, education, health records, and policy attributes. This fragmented data made it difficult to understand individual customer risk profiles, leading to challenges in product recommendations, fraud detection, and targeted marketing.
Challenges and Objectives:
The primary challenges faced by the insurance provider were:
- 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
The key objectives for implementing the ML-based solution were:
- Enhanced Customer Understanding: Gain a 360-degree view of customers through comprehensive data analysis
- Improved Risk Assessment: Develop accurate and efficient risk profiles for individual customers
- Personalized Product Recommendations: Propose relevant products based on risk profiles and individual needs
- Targeted Marketing Campaigns: Optimize marketing efforts by targeting specific customer segments.
Solution:
BluePi addressed these challenges by developing a customized Customer Risk Profiling System powered by Machine Learning. The solution leveraged AWS services like EC2, S3, Redshift, EMR, and Glue to achieve the following:
- 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.
The Impact: Measurable Success and Enhanced Relationships
BluePi’s solution delivered tangible benefits to the DTH company:
Business Results and Benefits:
The implementation of the ML-based risk profiling system yielded significant benefits for the life insurance provider:
- 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.
- Boosted revenue and profitability.
- Enhanced marketing effectiveness and reach.
- Data-driven strategic decision-making.
- Fortified relationships with channel partners and consumers.
Technology Deployed:
- AWS EC2: Provided compute resources for building and running the ML model.
- AWS S3: Stored the integrated customer data.
- AWS Redshift: Facilitated data warehousing and analysis.
- AWS EMR: Supported the training and execution of the ML model.
- AWS Glue: Orchestrated the data extraction, transformation, and loading process.
Conclusion:
The implementation of the ML-based Customer Risk Profiling System successfully addressed the challenges faced by the life insurance provider. The solution improved customer understanding, enhanced risk assessment, personalized product recommendations, and optimized marketing efforts, resulting in increased customer satisfaction, improved sales, and reduced operational costs. This case study demonstrates the potential of Machine Learning in transforming customer experience and driving business growth within the BFSI sector.
