AI and Data: How Insurers Can Leverage It Positively

AI and data analytics are revolutionising the specialty insurance sector, and Aspen is embracing this transformation. These technologies are enhancing traditional methods, leading to more precise risk assessments, swifter claims processing, and overall improved efficiency. In this blog, I will discuss how insurers can use AI for positive change. 

The true success of AI in insurance depends on getting your core data foundations right. Effective data governance—spanning people, processes, and the management of data availability and quality—is critical. Without a solid data foundation, AI simply will not work. At Aspen, we are addressing this by strengthening our data management strategies, which ensures that the AI we deploy can deliver the best results. 

Traditionally, underwriters have used their knowledge and a limited dataset to gauge risks. AI-driven analytics can significantly enhance this process by providing numerous data points and insights. For example, when insuring a marine vessel, AI can examine hundreds of data points including the vessel’s condition, history, maintenance records, and external factors like weather. Advanced models help insurers better correlate these variables, producing more accurate risk profiles. This approach complements rather than replaces the underwriter’s expertise. 

To fully reap the benefits of AI, insurers must seamlessly integrate these technologies into their existing frameworks. The key is to embed AI augmentation within the underwriter’s workflow, presenting insights in a clear and accessible manner. Many AI processes, operate in the background, but their outputs must be easy to understand so that underwriters can readily see how a specific decision or price was derived.  

AI’s effectiveness is not just limited to structured data; it also needs to handle unstructured data, which remains a challenge for the insurance industry. Unstructured data, such as emails, documents, and images, is an untapped resource for many insurers but holds valuable information that can enhance risk assessments and claims processes. Successfully leveraging AI to process this unstructured data is still a hurdle but overcoming it will significantly increase operational efficiency and precision. 

At Aspen, we’ve launched Aspen Data Labs, focusing on applying AI and data to improve underwriting and claims procedures. Insurance heavily depends on assessing and pricing risk, and a vast amount of data amplifies this comprehension. AI aids in creating detailed models to swiftly evaluate risks and determine premiums. We’ve developed a platform within Aspen, and with our partners, to quickly generate solutions to business-identified opportunities. Our Data Labs team is built up of individuals who not only grasp specialty insurance processes but also possess data science and engineering expertise to devise solutions that add value to the organisation. 

When developing these solutions, it’s crucial to tackle ethical concerns. Transparency is vital; hence, we’ve established an AI oversight committee to monitor decision-making processes and ensure impartiality in our systems. Protecting data is equally important, particularly since personal information may be utilised in risk evaluations. Moreover, accountability is essential; insurers must take responsibility for any poor decisions made by AI. Human supervision is necessary to maintain fairness and precision in AI functions. 

At Aspen, our approach to AI integration is centred around three main areas. First, we enhance data ingestion through machine learning and process automation to refine internal operations. Second, we provide underwriters and claims specialists with deeper insights, allowing them to concentrate on high-value tasks and improve loss ratios. Third, we utilise AI to analyse intricate digital assets, unveiling patterns and insights that would otherwise take weeks to discover. 

As insurers continue to adopt AI and data analytics, those who do so ethically and efficiently, while addressing the complexities of data governance and the challenges of unstructured data, will not only boost their operational productivity but also establish themselves as leaders in a rapidly evolving market.