Artificial Intelligence (AI) is becoming a significant factor in the re/insurance industry, traditionally focused on risk analysis and management. AI has the potential to transform this sector, particularly through big data processing and analysis.
According to insights from Matthieu Despréaux, audit director, insurance at PwC Luxembourg, many insurance companies are already developing pilot projects to explore how AI can improve operational processes or customer experiences.
However, Despréaux noted that captives - insurance subsidiaries owned by a parent company - are not always prioritized in these efforts, as their parent companies tend to focus on core business activities.
Despréaux explained that the term "AI" is often used broadly, encompassing various new technologies that may not strictly involve AI. In many cases, the automation of processes in insurance is categorized under AI, though it might not fully involve AI’s capabilities.
He highlighted that beyond automation and cost optimization, captives will face more complex challenges when attempting to integrate AI into their operations in the near future.
One of the primary obstacles, according to Despréaux, is the complexity of integrating AI into existing infrastructure. Many insurance companies lack the necessary tools to deploy AI solutions smoothly, which makes project planning and execution difficult.
These initiatives also often come with high costs, and despite AI’s potential to automate tasks such as claim payments and risk management, the expected efficiency gains are not yet sufficient to justify the high investment.
Despréaux added that once AI is implemented, another challenge arises: employee training. Workers must be trained to use AI technologies and to process and analyze the data generated by AI systems to achieve long-term efficiency gains. AI’s effectiveness depends on accurate and complete data, but Despréaux pointed out that data quality varies widely, presenting a significant challenge.
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Most captives possess historical data that could support AI models, yet this data has not been structured for AI use. In addition, third parties such as captive managers and brokers would need to transform their operations to meet captives’ data requirements.
As technology continues to evolve, Despréaux believes AI will eventually enable innovations such as machine learning and predictive analysis. These advances could improve risk projection and pricing models. Some reinsurers are already conducting studies to assess climate risks and optimize their pricing and exposure models using AI.
However, data security and confidentiality are critical concerns in AI deployment. Despréaux noted that captives must comply with local and international regulations regarding AI use. There are also concerns about data security, particularly in relation to economic competition between global powers.
For example, the US Cloud Act allows American authorities to access data stored by US companies, even if the data is held outside the US. This raises concerns for international reinsurance companies, which need to ensure their sensitive data is protected from competitors and economic espionage.
Despite these challenges, Despréaux asserted that the obstacles to AI implementation are not insurmountable. With strategic planning and thoughtful investment, captives can gradually overcome the hurdles posed by technological disruption.
AI offers significant potential benefits, including improved operational efficiency, more accurate risk analysis, and enhanced customer experiences. Despréaux believes these factors will drive captives to adopt AI and simplify their operations.
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