As an insurance executive, you know that disasters can strike at any time, leaving your customers vulnerable and your business at risk. With the increasing severity and unpredictability of weather-related disasters, it’s more important than ever to have a solid disaster preparedness and response plan in place. That’s where insurance analytics comes in.

According to Robert Clark, founder and CEO of Cloverleaf Analytics, insurance analytics can bridge insurer operational data, customer data, and third-party data in visually engaging manners, allowing insurers to be nimbler in helping disaster-wrought regions. In his article for Forbes, Clark outlines several ways insurance analytics can help protect insurers and customers before and after disasters.

Advanced Forecasting of High-Risk Areas

Wildfires, tornadoes, and storms have significantly impacted the nation in recent years, often hitting the same states and cities repeatedly. But with modern insurance analytics, insurers can use historical data, third-party data, and information from claims to forecast the likely range of annual disasters, premiums, and claims for the next season. By cleaning up and organizing their data into one single data lake, insurers can make advanced data analysis easier and be better prepared for future disasters.

Proactive Warning

Seasonal weather data becomes available months in advance, providing insurers with critical insights to enhance forecasting and issue proactive warnings to their customers. Insurers should aim to be faster than government agencies in alerting policyholders and educating the uninsured on the importance of preparing for the coming season. This approach is not about generating revenue; it’s about safeguarding lives, protecting property, and strengthening customer relationships.

Protecting the Unprotected from Fraud

When a community is preparing or recovering from a disaster, the last thing an insurer wants to deal with is fraud. Insurance analytics, machine learning, business intelligence, and artificial intelligence can provide rapid analysis of customer behavior so insurers can send out messages about claims processes and the consequences of claims fraud to certain communities that are rife with this type of activity. With a well-organized insurance data management strategy, these technologies can deliver tremendous benefits to the insurer and insured within a minute, enabling expedited claims management and processing to help the insured get themselves back on their feet.

Building a Future-Proof Insurance Data Management Strategy

To have effective data management to support expedited claims management and processing, insurers should look to operational best practices such as standardizing data collection processes. Thanks to sensors, mobile technology, machine learning, and artificial intelligence, the amount of rich data about consumer and business activities continues to increase. By building a future-proof insurance data management strategy, insurers can be better prepared for future disasters and provide better protection for their customers.

In conclusion, insurance analytics is a powerful tool that can help protect insurers and customers before and after disasters. By using historical data, third-party data, and information from claims, insurers can forecast the likely range of annual disasters, premiums, and claims for the next season. By issuing proactive warnings to customers and educating the uninsured on the importance of disaster preparedness, insurers can safeguard lives, protect property, and strengthen customer relationships. And by using machine learning, business intelligence, and artificial intelligence to analyze customer behavior and prevent fraud, insurers can expedite claims management and processing to help the insured get themselves back on their feet.

At Riskwolf, we enable insurers to build and operate parametric insurance at scale using unique real-time data and dynamic risk modeling. Contact us today to learn more about how we can help you turn real-time data into insurance.

Source: Forbes