SIRC: Automation to address repetitive, manual tasks deliver benefits to all stakeholders: Guy Carpenter

November 5 2025 by

In what ways has digitalisation improved operational efficiency within reinsurance brokerage operations, and what specific technologies have had the most significant impact?  

Jacob Hughes: From a servicing perspective, the initial focus of automation is not always on the flashiest applications but on addressing repetitive, manual tasks which deliver benefits to all stakeholders along the reinsurance value chain. 

Specifically, we have been focusing on automating the ingestion of data to reduce turnaround times, enhance controls and minimise manual keying errors. This practical approach has already delivered significant success, with some processes benefiting from over 95% shorter cycle times through the deployment of bots.

By automating these routine tasks, we free up resources to reinvest in stronger controls and more value-added service to our clients and markets, ultimately improving both efficiency and service quality. 

In Asia Pacific, we are also looking to accelerate our implementation of B2B messaging with markets already working with Guy Carpenter in other regions, and regional APAC markets. B2B messaging delivers significant benefits to both our clients and markets, and we have significant ambitions to increase the proportion of our markets enabled with B2B with Guy Carpenter over the coming years. 

How is AI transforming claims processing workflows in the reinsurance sector, and what tangible operational efficiencies are being realized?  

JH: Globally, we have launched a variety of initiatives to enable rapid and accurate data transfer, and to collaborate with our clients on the ingestion and straight-through processing of claims. These initiatives have resulted in dramatically improved turnaround times. 

What advanced analytics capabilities are being integrated into risk assessment models to improve accuracy and predictive power? 

Kitty Bao: Our risk assessment models are significantly enhanced through the integration of advanced analytics capabilities. This process begins with comprehensive data preparation, encompassing the collection, cleaning, structuring and transformation of vast datasets to ensure high-quality and data-driven insights.  

We then leverage machine learning techniques to refine price and loss prediction, enabling us to extract deeper data insights. In addition, AI models are employed in cyclone prediction, allowing for more precise forecasting and risk mitigation. Together, these advanced analytics tools empower us to deliver more robust, data-driven risk assessments that support informed decision-making.

What challenges do organisations face when implementing AI-driven solutions and advanced analytics in their operations, and how can they overcome these obstacles? – 

JH and KB: Organisations face several challenges when implementing AI-driven solutions and advanced analytics, including regulatory compliance, data quality, user adoption and quality control.  

At Marsh McLennan, we have implemented an Artificial Intelligence Risk Framework to ensure we comply with AI regulations and that our usage of AI is legal and safe.  

To overcome data quality issues, we deploy automated systems for data collection and aggregation to reduce human error.  

For smooth AI adoption across generations, we invest in intuitive and user-friendly interfaces, comprehensive training programs and strategic talent acquisition. 

Finally, we maintain quality control by enforcing a robust peer review framework to validate AI-generated results and prevent over-reliance on AI outputs. 

 

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