Table of contents
About the client
The client is a leading global mutual insurance association specialising in maritime protection and indemnity (P&I) coverage, serving a wide range of international shipping clients.
Business challenge
Following a major merger, the client needed to integrate systems and data from two large organisations with decades of legacy applications and processes. The lack of consistency between internal systems made reporting inefficient and risky.
In the insurance industry, accurate data is essential for measuring the effectiveness of operations. One of the most important indicators is the loss ratio – the relationship between premiums collected and claims paid.
For a newly merged company, calculating this indicator using unified, granular and reliable data was critical for effective underwriting, risk assessment and planning business strategy.
Future Processing: a trusted partner with proven domain expertise
Our involvement with the client spans several years and includes various projects, giving us a deep understanding of the insurance domain.
For this project, we were brought in due to seamless business relation and marine insurance domain understanding, as onboarding a team without insurance experience would have taken months.
The marine insurance sector is highly specialised, and our prior collaboration allowed us to jump in and start delivering results immediately.
Centralised data platform
The main goal was to bring together all the client’s data – from legacy applications, SQL databases, Excel reports, and external sources – into one central platform. We played a vital role in the implementation of the data platform in Databricks, which enables scalable cloud-based data processing.
Data from different sources is loaded via Azure Data Factory, where it is orchestrated and validated.
The data is then processed and stored in a Delta Lake, an optimised columnar format that supports efficient querying, versioning, and reporting. This setup provides the foundation for advanced business analytics, including loss ratio calculations, which are critical for evaluating risk and profitability per member.
The platform is designed to be accessible across the organisation, with role-based permissions to ensure appropriate access levels. It also features a built-in AI assistant that accelerates data exploration and analysis by enabling even non-technical users to generate queries, build visualisations, and access documentation using natural language. This improves productivity and lowers the barrier to working with complex datasets, enabling users to work independently of technical teams.
From data to decisions
The platform integrates multiple types of data, including:
- Claims data and underwriting records
- Excel files from financial departments with cost breakdowns (management costs, reinsurance costs)
- AIS (Automatic Identification System) data showing vessel positions and docking times – useful for analysing behaviour and determining insurance premiums
This data environment enables the generation of business-critical metrics, using both simple and more advanced insurance models. Some calculations required complex data transformations and even manual data extraction from reinsurance contracts – dozens of documents, several exceeding 100 pages in length.
The resulting reports provide essential insights for operational and strategic decision-making.
Key outputs include:
Loss ratio report – which reflects the overall amount of incurred losses to the total amount of earned premiums
Combined loss ratio report – which goes a step further by also factoring in administrative, operational and reinsurance-related costs
Exposure report – which outlines the extent of insured risks across member groups and assets, helping assess potential liabilities and inform underwriting decisions
These reports provide actionable insights that support risk evaluation, premium calibration, claims forecasting, and loss prevention planning.
Loss ratio - now calculated better, used more strategically
Previously, the loss ratio was calculated using a basic claims-to-premium ratio. With the new platform, it now includes a broader range of cost components (such as reinsurance and management costs), and allows analysis at multiple granularity levels – down to individual vessels.
This makes it possible to:
- Identify members with unsustainable claim ratios
- Adjust premiums fairly and transparently
- Reduce risk through better forecasting and member behaviour analysis
- Maintain trust and loyalty within the insurance pool
The loss ratio is not just a financial metric—it’s a decision-making tool used across departments to align pricing with actual risk and promote long-term sustainability of the insurance group.
Who uses the platform
The platform is designed for cross-organisational use and is already leveraged by:
- underwriting teams during policy renewals
- claims departments for trend analysis
- C-level executives for monitoring overall financial health
- loss prevention teams to identify high-risk patterns and mitigate future losses
Ultimately, the goal is to make the platform the central source of reporting for all departments.
Main benefits of our partnership
The implementation of the data platform was an important milestone in the client’s digital transformation, replacing fragmented systems with a modern, cloud-based solution that enables advanced analytics and faster response to change.
This success was made possible by our team’s deep insurance domain expertise, allowing us to support the client from day one with a clear understanding of data, reporting, and business needs.
Key benefits of our collaboration include:
- Centralisation of all critical data in a cloud-based Databricks platform, improving accessibility and consistency across the organisation
- Automated daily data integration and validation enabled by Azure Data Factory, supporting up-to-date and reliable reporting
- Accurate calculation of business-critical indicators based on harmonised and high-quality data
- Enhanced decision-making and risk management, powered by integration with external data sources such as AIS
- A scalable foundation for further innovation, including AI-driven analytics and self-service reporting across departments
Technologies used in the project
