How big, small and wide data reduce insurance risk

Big data, small data and the “smart” insurance customer

By Jess Hurley
P&C and general insurance global markets lead at EIS.

Insurers around the globe are optimistic about and preparing for a post-pandemic environment. Despite current optimism, uncertainties still exist, including climate change, inflation’s impact on global supply chains, the Great Resignation, and the pandemic itself, which still isn’t over. The range of risks insurers face is broader than ever.

Our new whitepaper co-authored with Business Agility, and Endava Company, a digital transformation provider for the insurance industry, and AWS explores the ways smart data and networks reduce risk for insurers. The article covers the difference between big data, small data and wide data, the “smart” customer experience, risk in the age of smart data and more. Here’s a preview:

Big data, small data, wide data: Why do they matter for insurance and risk?

The amount of data insurers must process has grown exponentially, particularly in the past decade or so with the advancement of video streaming and the Internet of Things (IoT). In the last few decades, insurers have garnered a wealth of data now stored within big data. Improved data access has been beneficial to underwriting and claims, but those gains have plateaued.

Big data platforms can store vast reservoirs of information, such as a vehicle database specifying every constituent nut and bolt. However, that big data platform, by itself, won’t show the insurer the relationship between vehicles underwritten and a sudden surge of catalytic converter theft for a particular make or model. For that, we need to meld big data with small data.

Big and small data can work happily together. Small data, provided by a variety of sources, including for example from connected devices (IoT), when analysed against existing big data can help insurers with risk management. For instance, customers’ flood sensor data (small data) can be aggregated and then mapped against big data to manage and predict the spread of major flood events.

Wide data is the combination of disparate datasets, with the AI to interpolate those insights to identify risk or opportunity. Those disparate data sources might be siloed, and ordinarily are more difficult to extract. When brought together, small data and wide data, perhaps seemingly unrelated, are interpolated with big data to gain new insights. This is convergent smart data, which sits at the heart of future insurance risk management.

The ‘smart’ insurance customer experience

As technology continues to advance in the insurance industry, smart data opportunities are easy to overlook, but definitely profitable for insurers willing to embrace change. Low-cost, high-volume insurance claims are expensive to handle manually, and automation only goes so far. Touchless claims, powered by algorithms, smart data, and AI smooth the customer journey, allowing touchpoints prompted by the customer to be adjudicated instantly.

Touchpoint customer management powered by smart data enables touchpoint interactions to be timely and relevant. With data-driven customer experiences, those touchpoints and experiences will improve and change over time, as the smart data improves, and the AI learns. In the era of GDPR, customer data management also keeps customer data private across geographic boundaries while running an omnichannel customer centric system. Insurers that integrate smart data into their processes will benefit by creating a more efficient, improved customer experience.

As the speed of change accelerates and uncertain economic and environmental conditions linger, managing risk will be more important than ever in the insurance marketplace. Is your company ready to harness the potential of smart data and networks to manage that risk?

Check out our whitepaper, “How smart data and networks continue to reduce risk for insurers,” to learn more, and book a call to find out how EIS can help you reduce risk.