Tailored insights at the click of a button

Artificial intelligence and advanced analytics enable insurers to deliver tailored insights quicker, more accurately, and with greater scale and depth, according to Kirill Pankratov, head of transformation at Zurich Commercial Insurance.

Businesses have long relied on insurers for expert advice and insights on risk and loss prevention. Yet delivering actionable insights tailored to the specific needs of an individual customer is a far-from-simple task for insurers.

For instance, to address a request from a global customer, insurers traditionally faced the laborious task of collecting data from various countries, policies, and periods to compile an in-depth report on the customer’s risk profile and the effectiveness of their insurance programs. This intensive process was not only time consuming but also difficult to conduct on a regular basis. Sifting through the vast amount of information to identify key insights for an international company was as challenging as finding a needle in a haystack.

Customer-centric analytics

By using artificial intelligence (AI) and advanced analytics, insurers can deliver a range of tailored insights to customers. Technology enables the automation of data gathering, capturing the collective knowledge within the industry, and the creation of analytics tailored to the individual needs and business profile of each customer. At the click of a button, it can be possible to generate customised analyses that reveal loss trends, bring to attention potential future claims, or offer data-driven exposure evaluations to bolster risk management strategies.

For example, AI can support risk management in the retail sector by meticulously analysing claims data to identify liability hotspots. It can discern specific store layouts or equipment linked with higher accident rates, allowing insurers to proactively recommend adjustments in retailers’ setups, thereby diminishing the risk of incidents to customers and employees in the stores.

Zurich has leveraged the strengths of its data analytics platform for underwriting to offer an application that enhances engagement with its commercial customers. This application merges a comprehensive library of risk-based insights with dynamic analytic tools, delivering highly relevant insights to each customer. With content curated by Zurich’s underwriters, risk service professionals and customer managers, the output from this application is designed to foster impactful conversations and support informed decision making in risk management actions and insurance solutions.

Revealing loss trends

Demand for insights into potential risk and claim scenarios continues to increase as more and more companies want to better understand how to build stronger resilience. In response to this trend, the development of AI-powered analytics tools has been accelerated, which surpasses the capabilities of traditional analysis in detecting nuanced exposure and loss patterns.

For example, when assessing the risk of natural disasters across a global portfolio of physical assets, the traditional approach may rely on historical data that overlooks emerging trends or subtle correlations. However, with advanced analytics methods, it is possible to detect under-the-radar patterns, such as the frequency of localised climatic events affecting dispersed assets. This insight can inform discussions on pre-emptive measures and tailored insurance solutions with customers.

Analytics tools also extend beyond reactive measures to proactive advice. They enable insurers to explore potential loss scenarios within specific industries or geographic locations, offering customers insights into potential risks they may not have previously considered. This level of specificity could be of value for customers who, for instance, contemplate expansion or diversification, as it allows them to make decisions with a fuller understanding of the risk landscape.

Bespoke benchmarking

Another area of demand for analytics is benchmarking. Benchmarking evolves to become much more bespoke, now often encompassing details about risk factors, exposures, perils, losses and insurance coverages. As the complexity of corporate risks grows, so too must the sophistication of benchmarking methodologies.

The industry is responding with innovative analytics tools that attempt to construct ‘virtual models’ of customer businesses, integrating relevant claims and exposure data from extensive portfolios of similar risks. These applications aim to provide benchmarks that are not only more accurate but also deeply reflective of companies’ true risk profiles.

These analytics capabilities also allow for the more precise assessment and comparison of the quality of risk controls at customers’ sites, identifying opportunities for further improvement. Such insights enable risk managers to engage in impactful internal dialogues focused on enhancing risk mitigation measures.

Similarly, automated analytics can be applied to evaluations of insurable values. It can be possible to automatically produce analysis of property values, showing changes in valuations over time and measured against inflation. This helps identify potential undervalued assets and protect customers from underinsurance.

Key role for risk managers

While technology can automate and enhance the delivery of analytics, humans should continue to play a pivotal role. Advanced analytics, powered by AI, generate insights that foster dialogue and inform decision making. However, it is essential for individuals to ask the right questions, refine the processes, and verify the outcomes.

For this to happen, risk managers will need to grasp both the capabilities and limitations of AI as well as its application and importance within their own organisations. The latest annual McKinsey Global Survey on the current state of AI indicates that 40% of respondents say their organisations will increase their investment in AI. Furthermore, the European Union is in the process of enacting a regulatory framework on AI, a movement echoed or considered by a number of other jurisdictions worldwide. Consequently, in addition to understanding their own organisations’ policies and compliance around the use of AI, internally, and through third parties, risk managers will need to understand the regulatory environment in which they operate.

Risk managers have a critical role to play in shaping the use of advanced analytics and AI in risk and insurance. They need to clearly articulate the risk and business problems that require solutions. The opportunity for risk managers and insurers alike is to think beyond historical interactions. Although analytics and AI may not resolve every challenge, their targeted application has the potential to significantly advance our collective capabilities in the field.

Contributed by Kirill Pankratov, head of transformation at Zurich Commercial Insurance

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