Modernizing Actuarial Insights with Azure Data Lakehouse for Faster, Smarter Risk Decisions

Modernizing Actuarial Insights with Azure Data Lakehouse for Faster, Smarter Risk Decisions

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Client

The client is a leading insurance and risk management firm specializing in business insurance, employee health and benefits, retirement solutions, and private client services. Headquartered in North America, the firm leverages its deep industry expertise and advisory capabilities to help businesses and individuals navigate complex risks. With a strong market presence, it delivers tailored solutions that enhance financial security and resilience.

Market Trends

The insurance industry faces growing complexity due to evolving risks, regulatory demands, and rising customer expectations. Cloud-based platforms are increasingly adopted to enhance underwriting, actuarial modeling, and claims processing. Real-time risk insights are now critical as cyber threats and economic volatility escalate.

However, many insurers still rely on legacy systems that lack the scalability and analytics needed to process vast actuarial datasets efficiently. These outdated platforms hinder data integration, slow decision-making, and create compliance challenges. To stay competitive, insurers must modernize their data ecosystems, enabling AI-driven insights, operational efficiency, and improved risk management.

Need for Change

To keep pace with industry demands, the client needed to modernize its actuarial and risk assessment processes. Their on-premises data warehouse struggled with growing data complexity, causing inefficiencies in underwriting, risk evaluation, and decision-making.

Scalability and performance limitations

The legacy system struggled to process large actuarial datasets, causing delays in underwriting and risk evaluation.

Siloed and fragmented data

Risk insights were dispersed across multiple sources, making it difficult to generate a unified view of exposures and market trends.

Manual and inefficient workflows

Manual data consolidation and processing required manual effort, leading to delays and reducing responsiveness to market shifts.

Limited advanced analytics

The existing infrastructure lacked the ability to support AI-driven actuarial models and predictive risk analysis.

Compliance and security concerns

With evolving regulatory frameworks like IFRS 17 and Solvency II, the existing setup lacked the governance needed to ensure data accuracy and security.

To overcome these challenges, the client required a future-ready, cloud-based actuarial data platform. This could enhance risk modeling, enable real-time insights, and improve overall operational efficiency.

Solution

LTIMindtree collaborated with the client to move from an outdated, on-premises system to a secure and scalable Azure Data Lakehouse, delivering:

Reliable and scalable platform

Migrated from a traditional on-premises data warehouse to an enterprise-grade Azure Data Lakehouse, ensuring high availability and the ability to handle large actuarial datasets efficiently.

Automated and integrated risk insights

Combined data from multiple sources, leveraging Azure Data Factory and Databricks workflows to streamline actuarial data ingestion, consolidation, and transformation.

Common data model for data consistency

Established a standardized data architecture aligned with insurance regulatory requirements, reducing discrepancies and improving analytics accuracy.

Agile and DevOps-driven delivery

Implemented a DevOps-based iterative deployment model, enabling faster value realization through incremental sprints and enhanced operational efficiency.

Tech Stack

Business Benefits

Reduced actuarial data processing time by 60% with automated workflows.
Streamlined underwriting and risk assessment through automation and improved data access.
Enhanced actuarial insights, enabling better decision-making for risk advisors.
Established a scalable, cloud-based infrastructure for long-term flexibility and cost efficiency.

Conclusion

With the shift to an Azure Data Lakehouse, the client eliminated inefficiencies from its legacy system, improved compliance with evolving regulations, and enhanced actuarial decision-making. This transformation now enables faster risk assessments, automation, and AI-driven insights, ensuring the firm stays ahead in an evolving insurance landscape.

In today’s dynamic insurance landscape, actuarial precision and real-time risk assessment are critical for smarter underwriting decisions. By modernizing their actuarial data ecosystem with Azure Data Lakehouse, our client has unlocked the power of AI-driven insights, streamlined risk modeling, and enhanced compliance. At LTIMindtree, we help insurers stay ahead of the curve by building future-ready, scalable, and intelligent data platforms.

Aniruddha Vaidya, Vice President- Data & Analytics, LTIMindtree

Looking to modernize your data platform? Connect with us at data.analytics@ltimindtree.com to explore scalable cloud transformation solutions.

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