**London**: Craig Campestre, Chief Revenue Officer at Earnix, discusses the need for deeper AI integration in the insurance sector, highlighting its potential in enhancing risk management, pricing, and climate risk assessment while stressing the importance of governance and the role of Chief AI Officers in driving strategic decisions.
In a recent discussion with Reinsurance News, Craig Campestre, Chief Revenue Officer at Earnix, expressed that the insurance and reinsurance sector has significant opportunities to enhance the utilisation of artificial intelligence (AI). He highlighted the necessity for a more profound integration of AI into business processes, alongside improved governance structures and the increasing prevalence of roles such as Chief AI Officer (CAIO).
Campestre stated that while strides have been made in AI adoption, “there is still room for broader and deeper AI adoption.” He pointed out the potential for re/insurers to leverage AI in portfolio diversification and in stress-testing exposures to emerging risks such as cyber threats and climate change. Furthermore, he underscored the importance of standardising AI governance frameworks to maintain fairness, transparency, and accountability in its application, urging re/insurers to focus on developing in-house expertise to fully harness AI’s capabilities.
“This includes fostering digital literacy and promoting cross-functional collaboration to integrate AI seamlessly into both day-to-day operations and long-term strategies,” Campestre elaborated. The establishment of the CAIO role signifies a strategic shift towards embedding AI in fundamental decision-making processes. According to Campestre, “A CAIO provides dedicated oversight for AI initiatives, ensures alignment with business goals, and bridges the gap between technical teams and executive leadership.” This role is increasingly viewed as a best practice within organisations aiming to remain agile, compliant, and innovative as they head toward 2025 and beyond.
Campestre offered insight into how AI is progressing from a tool primarily for optimisation to becoming a key driver for strategic decision-making throughout the re/insurance value chain. He discussed various areas where AI demonstrates notable efficacy, including pricing, risk analytics, and claims management. For example, he noted that “re/insurers are now deploying AI-driven pricing engines that offer a more granular understanding of risk,” allowing for adaptive pricing structures based on real-time market conditions.
Moreover, AI’s capabilities extend to loss reserving and fraud detection. Campestre indicated that AI improves reserve adequacy by accurately projecting future liabilities, utilising a diverse set of data points such as claims history and economic indicators. He remarked that “Machine learning algorithms are being used to identify fraudulent claims patterns with exceptional accuracy,” which not only reduces losses but also optimises the process for legitimate claims, resulting in faster, more reliable payouts.
AI plays a significant role in managing climate risk as well. Campestre highlighted that it enables re/insurers to model complex situations like rising sea levels and extreme weather, thus improving underwriting accuracy and revealing protection gaps. “Re/insurers are also using AI to enhance capacity in climate-vulnerable areas like Florida,” he said, stating that incorporating detailed climate data with predictive models aids in better risk assessment and resource allocation.
The discussion also delved into the transformative potential of generative AI (GenAI) within the re/insurance industry. Campestre identified its capabilities in automating various tasks, such as generating policy documents, summarising claims, and enhancing customer communications. He noted that GenAI could facilitate scenario modelling and portfolio optimisation via tailored simulations derived from market and environmental data.
Nevertheless, he acknowledged that challenges remain. Campestre stressed that ensuring the accuracy and ethical deployment of AI is paramount, as data bias and model hallucination can undermine stakeholder trust. Additionally, integrating GenAI into legacy systems poses hurdles, often requiring considerable investment and organisational adjustment. He concluded with a reminder that regulatory compliance is vital, requiring re/insurers to prove that their AI solutions are both transparent and in line with industry standards.
Source: Noah Wire Services
Subscribe to Industry Updates
