Analytics to deliver new age of insights and services for customers

Insurers are further expanding the use of advanced analytics to create more value for customers, writes Kirill Pankratov, Global Head of Data Analytics & Learning for Commercial Insurance.

With the current hype around artificial intelligence (AI), it helps to step back and consider the challenges we need to find solutions to and the value we as an insurer can bring to customers through technology.

The application of advanced analytics and AI within the insurance industry is now relatively evolved. In fact, insurers have been exploring uses of data analytics and AI for some time, and many are quite advanced in the process of integrating capabilities into the businesses.

Zurich, for example, has built a comprehensive commercial analytics platform, which continues to be rolled out globally for greater underwriting, claims and risk engineering insight. The platform provides a holistic view of risk, breaking down historical silos of products and lines of business, and enabling detailed analysis of risk from portfolio level, down to a single risk factor level.

 

Insights for customers

While still early days, analytics platforms like these will form part of the foundation of products and services going forward. To date, the industry’s implementation of data analytics has been predominantly internal, focused on improving efficiencies and enhancing core functions like underwriting and claims. But with analytics being embedded in insurers’ operations more and more, we are now entering a new phase in the development of data analytics that will see attention turn squarely to the needs of customers and distribution partners.

For example, analysis of granular data can help insurers identify trends, patterns, and correlations in data, enabling underwriters and risk engineers to offer insights and recommendations to strengthen customers’ business operations, as well as improve servicing for their own customers and other key stakeholder groups. AI tools are also increasingly being used to analyse large volumes of claims records, identifying trends that may be difficult or impossible to detect through conventional data analysis techniques. For example, we recently identified a pattern of sink hole claims in one geographical area for a customer and were able to advise them how to proactively address it for several affected locations in the future.

In another exciting application, geospatial data analytics enables insurers to more effectively assess the impact of natural and man-made disasters on customers’ property, assets and supply chains. By using satellite imagery, remote sensing, and geographic information systems, insurers can quickly estimate the extent of damages and deploy the necessary resources for claims handling. This prompter response enables customers to receive support and relief when they need it most, improving their overall experience with the insurers.

 

Tailored products and service

Advanced analytics also enables insurers to provide more precise risk and insurance program benchmarking, ensuring that customers receive comprehensive protection for their physical assets. By analysing granular data on factors like inflation, market conditions, and asset values, insurers can support customers with their efforts of estimating insured values more accurately. This level of precision aims to help customers purchase insurance limits that provide an adequate level of protection, fostering peace of mind and confidence in their coverage.

The adoption of application programming interfaces (APIs) and other connectivity methods can significantly improve data exchange between all parties involved in insurance transactions. Information on exposure, insurance program details, claims performance, and risk improvement recommendations can be exchanged seamlessly, enabling more efficient customer servicing. This increased automation allows all parties to spend more time analysing and discussing optimal insurance and service proposals for customers.

 

Deeper relationships

The application of advanced analytics for large commercial risks is a complex task that requires a combination of skills and competencies, including expertise in risk management, engineering, data science, and governance. As more and more data becomes available – both internal and external - risk managers will need support to develop their business use cases, to judge the relevance of data, validate and monitor models, and to understand the limitations and nuances of modelled output.

As experts in risk and analytics, insurers are in a great position to help risk managers and their businesses leverage their data to inform internal decision making, risk management, strategy, and investment. This has also been underlined by Jörg Bertogg, Chief Operating Officer at Zurich Commercial Insurance “Insurers have the expertise and capability to apply data analytics at scale, combining their own data and insights with those of customers and third parties.”

The increased sharing of data between insurers and insureds has the potential to further strengthen relationships with customers, spurring innovation and collaboration. Interactions will be more frequent and meaningful, as insurers share insights and collaborate even more on loss prevention and mitigation. And as analytics evolve, large commercial customers can expect more tailored products from insurers, as well as enhanced claims and risk servicing.

 

Differentiator

Advanced analytics methods continue to transform the commercial insurance industry. From more rapid assessment of damages to tailored risk servicing, streamlined data exchange, and insights for business optimization and broader value creation, these innovative tools and techniques are helping insurers meet the needs of commercial customers in a changing risk landscape.

Insurers that continue to embrace advanced analytics are well-positioned to succeed in an increasingly competitive market and enhance the overall customer experience.

Originally published in Commercial Risk on May 30, 2023