How can you get better insights from your data?

As the insurance industry becomes more data-driven, AI is one of the best tools to help interpret data, gain insights, spot trends and identify problems. If used effectively, it can be an ideal fit for improving risk assessment, claims, underwriting and fraud detection where understanding things like trends and themes play a critical part. AI can rapidly identify and analyse these numbers, often in a matter of seconds. For complicated products, such as commercial insurance, it may be vastly better at managing and verifying comprehensive coverage.

Another facet of AI, automation, is fundamental to supporting a greater and continuous flow of data into an insurer’s processes, and its efficiency benefits compound over time. The more data a firm can ingest, the more insights can be unlocked, as patterns emerge and new metrics can be calculated. This means risk managers aren’t just able to work with new data more quickly, but also be presented with a much larger and richer picture of their overall risk environment. It can make a fundamental difference to underwriting decisions.

This all means it is imperative that insurers have sophisticated tools and programs as part of their tech stack, to collect and ingest data at industrial scale. The use of machine learning can be a game changer. As its name suggests, machine learning relies on continuous learning – with programs gaining more strength and insights from the continuous flow of data they receive. If machine learning is used as part of an insurer’s automation solution, it will only improve the insights gained over time.

The latest evolution of AI has been the commercialisation of GenAI, a subset of artificial intelligence that can create human-like content, whether it is through text messages, images or sound.

Many companies are still in the early days of discovering this technology and understanding how to use it. However, there are some uses that have been demonstrated. For example, GenAI can fast-stream tasks such as information summarisation, content creation, intelligent search and coding. 

In a recent report from Moody’s Investors Service, analysts highlight how deeper integration could offer opportunities to boost operating efficiency, which could benefit insurers’ credit quality.

Analysts stated that combining the power of GenAI with insurers’ in-house data could result in providing a “seamless, natural language-based experience” for underwriters,  which ultimately will allow them to quickly synthesise multiple sources of information and enrich and accelerate their engagement with insurance buyers in coverage discussions.

Currently, risk modelling activities are undertaken by brokers and insurance companies, meaning insurance buyers have little or no visibility or understanding of the modelling process, inputs and outputs and are fully reliant on a broker or insurer.

For example, in CAT risk modelling, as demand for coverage for natural disasters has grown, so too has the need for models that can be relied upon during risk assessment. While CAT models have improved in quality and modelling capabilities, poor property reinsurance underwriting results in recent years have contributed to widely held perceptions that risk assessments are underestimating actual loss experience.

Accurate risk modelling helps insurance buyers to have a better picture of the exposures their assets have, and provides them with a way to attain the most suitable insurance coverages at the most optimum price.

While tools like Insurwave can streamline data assembly, making the process more efficient during renewals and initial coverage acquisition, it is important to remember that the quality of the output is only as good as its input. If the data you submit is of low quality, the output is unlikely to be any better.

However, the introduction of new alternatives, such as generative AI, represents a new frontier for obtaining more meaningful insights, promising human-like content creation and potential revolutions in underwriting. Reports suggest that combining AI with insurers' in-house data could create a seamless, natural language-based experience for underwriters, ultimately enhancing engagement with insurance buyers. 

As technology evolves, the greater the possibilities it can provide in aiding insurance teams to yield better insights from their data, offering solutions to enhance decision-making processes and meet evolving industry demands.