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 dened 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:
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
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
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
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
FAQs
1. What is actuarial analytics modernization, and why is it important for re-insurers?
Actuarial analytics modernization involves upgrading legacy actuarial systems with cloud, automation, and advanced analytics to improve efficiency, accuracy, scalability, and decision-making in re-insurance, while enabling faster insights and stronger alignment with evolving business and regulatory demands.
2. What challenges do re-insurers face with traditional actuarial data platforms?
Traditional actuarial platforms rely heavily on manual data integration, siloed systems, and static models, making experience studies time-consuming, error-prone, and difficult to scale or audit, especially as data volumes and reporting complexity continue to grow.
3. How does a cloud-based actuarial analytics platform improve experience studies?
A cloud-based platform centralizes data, automates ingestion and validation, and enables dynamic modeling—allowing actuaries to run faster, more accurate experience studies across regions and assumptions, without heavy dependency on IT teams or manual interventions.
4. How does automation enhance actuarial efficiency and governance?
Automation reduces manual data handling, ensures consistent data quality checks, enables audit tracking, and supports version-controlled models—resulting in faster actuarial studies and stronger governance, with improved transparency, repeatability, and reduced operational risk.
5. What role does advanced analytics play in modern actuarial decision-making?
Advanced analytics enables predictive and prescriptive modeling, scenario testing, and assumption-driven analysis, helping actuaries assess risk more accurately and respond to market or regulatory changes, while supporting more proactive and data-driven strategic decisions.
6. How does actuarial analytics modernization support regulatory compliance?
Modern actuarial platforms provide audit-ready data, standardized study frameworks, and traceable assumptions, helping re-insurers meet regulatory requirements with greater transparency and confidence, while reducing compliance effort and improving consistency across reporting cycles.