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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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The models are based on a static “current climate state” view derived from analysis of historical data sets.

“When catastrophe models are built, typically, the hazard module is locked down first and then the model is calibrated. As such, there is a delay between when the hazard is set and the model is released,” said Weatherhead.

“In the past, this wasn’t a significant issue, and the models were considered fit for current near-climate modelling, but given the rate of change now observed, they fall out of date quickly.”

However, there is a balance to be found between stability of modelled losses and keeping models up to date, he said.

“The (re)insurance industry does not want or expect models to change dynamically every year to reflect the latest view on climate, as this would make it difficult to also understand and manage the effects of portfolio growth,” Weatherhead added.

Rather, the current expectation is that models will only be updated every three to five years.

However, Weatherhead noted that “there are still many demands on insurers to assess their climate risk for regulatory or strategic planning purposes”.

This is driven by local regulators requiring climate-risk assessments.

However, the challenges are that different regulators choose different scenarios, so there is not  a single solution that can be applied everywhere.

“We need modelling of the baseline conditions to apply the adjustments, which can be difficult for Asia Pacific, where models don’t exist for all perils and countries,” he noted.

Guy Carpenter has developed flood models designed to allow for climate risk assessments for most countries, but there are still gaps for perils such as drought and impacts of excess heat and water shortage.

‘Secondary’ perils

While secondary perils is a misnomer, as they are driving significant loss, Weatherhead said: “The challenge in Asia is that these perils have by and large not been modelled, with the exception of Australia.”

The difficulty is that many insurance companies in the region have not yet developed internal expertise and/or technical tools to transform the raw scientific data into something that can be used for business decisions.

The challenge, according to Weatherhead, is how “we address the gap in knowledge”.

“This will change in time, but for now there is a reliance on experience-based loadings and external advice,” he added.

The newer generation of models from the larger commercial vendors, while very welcome, are still limited in their coverage and aim mostly at the larger, more established markets.

Plugging gaps

While Weatherhead does not see “too many problems with model credibility in the region, at least for the major countries and perils”, the bigger issue “is the quality of the data going into the models from the exposure side”.

“This is very mixed across the region. In some countries, detailed location-level data is the standard; in others, its data aggregated to province or even country level. However, we do see data improving every year,” he added.

Most insurance companies are working to improve their data, and there is a realisation that if they do not, they are at a disadvantage compared to their peers.

Location-level data allows for better representation of the splits between building and content values, and other secondary factors such as building height and construction.

“This can substantially change the modelling results and give a much-improved view of the risk,” he noted.

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