The Industry Landscape
The Need for Data Modernization in Re-insurance

The re-insurance industry relies heavily on data to assess risk, determine pricing and create financial strategies. With the exponential growth of data sources, companies are increasingly challenged to efficiently integrate, manage and analyze data. Implementing a seamless cloud-based ecosystem is crucial to enabling sophisticated data analysis, improving decision-making and maintaining a competitive advantage.

The Client Challenge
Building a Scalable, Unified Data Platform

The challenge for the client was to modernize its data platform for advanced analytics initiatives. The existing platform employed manual data integration across multiple systems – such as policy administration, enterprise resource planning, customer relationship management and third-party sources – and lacked the ability to manage demand 󹀹uctuations.

The insurer needed a scalable, unified platform to provide curated data for experience studies and detailed actuarial analyses.

Key strategic needs included:

Integrated Data Sources

Building a comprehensive, end-to-end analytics solution to centralize data from diverse sources

Enhanced Quality & Security

Ensuring centralized data quality checks while improving accuracy and integrating audit-ready data management

Experience Analysis Tool

Developing an experience analysis tool on a robust enterprise platform to enhance actuarial studies

 

Defined Frameworks

Establishing frameworks, best practices and accelerators to boost the output quality

Dynamic Actuarial Modeling

Creating a dynamic platform for actuaries to develop and deploy diverse data science models

 

The Solution
A Cloud-first Approach to Enterprise Data Management

WNS Analytics (WNS’ data, analytics and AI practice) combined human expertise and AI-powered solutions to design a smarter business for the client. Our data engineering experts designed and implemented a modern cloud-based enterprise data solution on the Microsoft Azure platform to automate data validation, ingestion and generation of actuarial study outcomes.

Our approach encompassed the following steps:

1. Source Integration and Data Standardization

• Migrating historical databases, including raw data and old study results, to a new platform – stored outside the main data model – to run ad hoc queries
• Integrating incremental data from various sources required to run experience studies
• Storing data in a standardized data model
• Enhancing the data platform to support data segregation by region and treaty

2. Data Integrity and Code Management

• Validating data against de󹀼ned data quality rules
• Using Extract, Transform and Load (ETL) to integrate, cleanse and consolidate data from multiple sources data into a centralized, standardized data model
• Managing code and data with versioning and access control

3. Deep Analysis and Accuracy for Experience Study

• Conducting studies covering different product types by country and region
• Enabling assumptions to run different studies

4. Automation

• Implementing data ingestion process based on dynamic data ingestion framework
• Enabling audit tracking of processed data
• Developing a User Interface (UI) to run risk exposures and actuarial studies across products and assumptions
• Integrating results from multiple models built for actuarial studies

Tech Stack

The main components of our solution included:

Optimized Platform Architecture

This featured:

A centralized, secure data repository tailored for experience studies

Seamless integration with analysis tools to run experience studies across product lines and assumptions

Flexible and Efficient Experience Analytics

The analytics platform:

Applied predictive and prescriptive analytics on all assumptions and parameters

Incorporated flexibility, allowing for adjustments in assumptions aligned with industry trends

Facilitated study models and testing, adhering to best practices and standards

Delivered results in standardized templates

Intuitive Automation

We streamlined operations and enhanced user experience by implementing:

User-friendly dynamic data ingestion process for data processing

End-to-end UI-driven process automation

UI-driven experience study analysis

UI screens enabled with multiple functions

Data Security, Quality and Audit

We deployed a comprehensive data security model to enforce role-based access controls on all data elements and auto-versioning on actuarial outcomes. The model enabled:

Centralized data quality checks

Audit-integrated data management

Version-controlled code execution

The Outcome
Scalable Actuarial Analytics and Streamlined Data Operations

By designing a smarter, AI-powered data foundation – and enabling human-led actuarial decision-making – WNS empowered the client to drive efficiency and insight. Benefits included:

 percent increase in efficiency of actuarial studies with enhanced functional and actuarial experience

Advanced actuarial analytics capabilities extending beyond traditional studies, enabling more sophisticated risk assessment and predictive modeling

~ percent increase in efficiency by streamlining data integration processes and accelerating experience study analysis

Enhanced operational efficiency by streamlining data ingestion, transformation and validation tasks through automation

Flexible, region-specific assumption adjustments aligned with industry developments, enabling actuaries to adapt swiftly to evolving market conditions and regulatory changes

Improved data access and insights through a centralized data model, ensuring a single version of truth and enhanced data accuracy and consistency across all studies

Improved compliance and methodological rigor through implementation of experience study models and testing aligned with industry best practices

Strengthened data quality and governance with increased control over data assets, supported by a robust governance framework, integrated audit capabilities and automated processes