How Reinsurers Can Get Started with AI for Risk Management | NTT DATA

Wed, 19 June 2024

How Reinsurers Can Get Started with AI for Risk Management

Reinventing Insurance: Reinsurers and Climate Change

Generally, reinsurers are content to stay out of the spotlight, managing risk and spreading exposure across the market. However, as Ian Smith pointed out in his recent Financial Times article, reinsurers are finding it harder to "fly under the radar."[i]  The shortcomings of conventional risk assessment tools have been thrust into the limelight by storms, wildfires, and hurricanes that occur with increasing frequency and severity.

So, how can reinsurers adapt to these new pressures and improve their risk management strategies? The answer lies in embracing advanced analytics and artificial intelligence (AI).


The Limitations of Traditional Models

Reinsurers have long relied on historical data and established models to predict and manage risks. These models, once the bedrock of the industry, are now proving less effective as the underlying patterns of risk change. Historical data can no longer reliably predict future events when those events are influenced by rapidly changing variables like climate change.

As unprecedented wildfires, floods, and hurricanes—or even supply chain disruptions such as the Red Sea shipping crisis—are occurring with greater frequency and severity, this has made the flaws in traditional tools and methods painfully clear.


Embracing Advanced Analytics and AI

One way for reinsurers to address these challenges is to turn to advanced analytics and artificial intelligence (AI). By leveraging machine learning and AI, reinsurers can significantly enhance the accuracy of their risk models.

As we’re all aware, machine learning algorithms can process vast amounts of data to identify patterns and predict outcomes with greater precision. This will allow reinsurers to dynamically respond to the evolving nature of risk, providing more accurate assessments and improving their ability to mitigate potential losses.


Case in Point: Machine Learning for Predictive Modelling

To lay out one example, machine learning-based solutions could analyse extensive datasets from diverse sources, such as satellite imagery, weather patterns, and socio-economic data, to create predictive models that are far more sophisticated than those based on historical data alone.

For instance, an algorithm could predict the likelihood of natural disasters in specific regions, enabling reinsurers to adjust their coverage and premiums accordingly. This proactive approach would not only improve risk management but also ensures better financial stability for both reinsurers and their clients.

To demonstrate the potential of AI in environmental analysis, we saw the winning entry in 2021’s Civil Service Data Challenge apply machine learning to satellite images to calculate changes in UK peatlands.


Navigating Regulatory Scrutiny

Another significant challenge for the reinsurance industry is increasing regulatory scrutiny, particularly in offshore jurisdictions. Reinsurers must ensure the integrity of their financial operations and comply with global standards to mitigate risks associated with illiquid investments and regulatory challenges. Advanced analytics and AI can play a crucial role here as well, providing tools to monitor compliance and detect anomalies that could indicate regulatory or sanctions breaches.

AI-driven compliance solutions can automate the monitoring of transactions and financial activities, flagging suspicious behaviour and ensuring adherence to regulatory requirements. By integrating these technologies, reinsurers can streamline their compliance processes, reducing the risk of regulatory penalties and improving their operational efficiency.


The Shift to Integrated Data Platforms

Another way for reinsurers to brace themselves against risk volatility is through integrated data platforms. These can consolidate data from multiple sources, providing a unified view of risk factors. A strategy involving such platforms would support advanced analytics, machine learning, and real-time data processing, allowing reinsurers to make informed decisions quickly.


The Path Forward

The environment in which reinsurers operate is not likely to become less complex. However, if firms can put the right technologies, dataflows, and processes in place – then the lives of analysts, actuaries, and underwriters might.

It is time for the industry to revolutionise how it thinks about data and embrace the tools that will shape the future of risk assessment. By adopting these technologies, reinsurers can enhance their risk models, navigate regulatory landscapes more effectively, and move beyond the limitations of traditional data tools. For reinsurers, the path forward is clear: innovate or risk being left behind.

If you’re a reinsurer looking to explore the potential of advanced analytics and AI, the first step is to set up a secure, scalable data pipeline and end-to-end data management system. For more detailed information, see how our data and intelligence services have helped clients like HM Treasury or the Met Office to transform their operations.

Alternatively, for a free consultation on setting up your next AI risk management project, get in touch with us today.


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