Life Insurance Co. Ltd., a prominent subsidiary of the Pacific Century Group, was founded in Asia in 2013. As a leader in the insurance sector, Life Insurance is dedicated to enhancing customer experiences through innovative, technology-driven solutions.
Key Challenges
Life Insurance struggled with fragmented data across 50 source systems. This fragmentation hindered effective decision-making, reduced operational efficiency, and limited the potential for data monetization. The company sought to unify its data to enable better decision-making and drive innovation.
Key Requirements
- Data Consolidation: Integrate and streamline data across all departments to eliminate silos.
- Timely Decision-Making: Provide relevant information promptly to stakeholders.
- Operational Efficiency: Enhance reconciliation processes, improve data quality, and increase transparency.
- Hyper-Personalization: Use behavioral data from all customer touchpoints to deliver highly personalized experiences.
Our Solution
Blazeclan delivered a robust solution to address Life Insurance’s data challenges and achieve their strategic objectives:
- AI & Analytics Use Cases: Implemented 29 advanced AI and analytics-driven use cases to leverage data effectively.
- Unified Data Hub (UDH): The UDH provided AI-assisted incremental business value by integrating real-time insights across touchpoints. This central hub improved decision-making and unlocked previously untapped data, enhancing the company’s ability to monetize its data assets.
- Optimized Data Investment: Enhanced the ROI of Life Insurance’s data lake and warehouse, facilitating valuable insights and supporting informed decision-making, which boosted overall operational efficiency.
Our Approach
Strategic Integration
- Databricks on AWS: Utilized Databricks on AWS to create a unified data platform combining the benefits of data warehouses and data lakes. This open Lakehouse architecture streamlined data management and consolidated all analytics and AI workloads into a single system.
- Microservices API: Developed and deployed a Microservices API to enable near real-time data consumption, allowing the business to access and drive datasets with minimal latency.
- AWS Native Services: Implemented robust monitoring and logging infrastructure using AWS native services like CloudWatch, SNS, and CloudTrail, providing comprehensive oversight and ensuring reliable performance and security.
Databricks Lakehouse Architecture Advantages
- Unified Analytics
Supports comprehensive analysis of both structured and unstructured data. - Versatile Workloads
Enables streaming and machine learning capabilities for advanced data analytics and business intelligence. - Scalability
Provides horizontal scaling to handle increasing data volumes without sacrificing performance. - Data Consistency
Ensures reliable data modifications with ACID transactions. - Simplified Architecture
Reduces the complexity of managing separate data warehouses and data lakes. - Cost Efficiency
Offers cost-effective storage with robust analytics capabilities.
Outcomes
Lead Cleansing & Enrichment:
- Developed a customizable dashboard that processed over 2.7 million leads.
- Generated an annual premium equivalent of 80.5 million THB.
- Reduced turnaround time by 96%, from 24 hours to under 1 hour, significantly enhancing operational efficiency.
Event-Driven Data Streaming for AI-Based Underwriting:
- Introduced real-time data triggers for timely processing and publishing.
- Improved data accuracy by processing source data in JSON format and aggregating it into AVRO format.
- Upgraded from legacy systems to real-time data streaming, enhancing data timeliness and accuracy.
Customer 360 Dashboard Migration:
- Transitioned from Tableau to Power BI, providing features like quick search, multi-language support, customer profile views, policy details, and sales & marketing insights.
Tech Stack
- Cloud Platform: AWS Public Cloud Hosting
- Data Platform: Databricks Lakehouse
- Data Streaming: Confluent Kafka
- Data Engineering: Spark Streaming & Batch Workloads
- API Management: Amazon EKS API & Gateway
- Advanced Analytics & Reporting: Power BI
- MLOps: Databricks MLOps Workflows
- Database Management: AWS RDS
- Monitoring & Logging: AWS CloudWatch, SNS, and DevOps tools