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Robert Clark is the founder and CEO of Cloverleaf Analytics , a leading provider of insurance intelligence solutions. Looking back to when I began my career in the insurance industry three decades ago, I would have laughed if someone had told me that within just five years, the entire industry would be transformed. Yet, this is exactly what's happened, accelerated by the impacts of Covid-19, generative AI and other major changes in healthcare and technology.
Additionally, weather-related disasters have become increasingly severe and destructive. This isn't a political statement—just consider the ongoing recovery efforts after recent hurricanes as evidence. These weather disasters pose risks to civilians, businesses and government infrastructure.
For insurers, there is unpredictability, but not everything about disaster preparedness or response is hard to forecast. With modern insurance analytics that can bridge insurer operational data, customer data and third-party data in visually engaging manners, insurers can be nimbler in helping disaster-wrought regions. Advanced Forecasting Of High-Risk Areas It's no secret that wildfires, tornadoes and storms have significantly impacted the nation in recent years, often hitting the same states and cities repeatedly.
But it's the unusual events, like what happened in Hawaii , that no data analytics can fully predict. Let’s take California as an example. Half of the 58 counties in California that are normally impacted by wildfires are high risk to very high risk .
There is enough historical insurer data, third-party data and information from California FAIR plan claims for insurers to forecast the likely range of annual disasters, premiums and claims for the next season. To do this type of analysis within the context of their business, insurers must work hard to clean up and organize their data into one single data lake. This will make advanced data analysis easier to enter, synthesize and evaluate new third-party data as events evolve.
iOS 18.1.1—Update Now Warning Issued To All iPhone Users Leak Reveals Trump Crypto Bombshell As Bitcoin Suddenly Surges Toward $100,000 Price Now Hackers Are Using Snail Mail In Cyber Attacks—Here’s How These same principles apply to hurricanes and tornadoes.
There are insurtech vendors like CAPE Analytics or reThoughtFlood that provide specialized risk analysis of wildfires or floods, but before a company can fully benefit from these types of services, they need data cleanliness and organization done right. 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. For the consumer, fraud will impact the future products and services available to them if an insurer feels they are more at risk of false claims.
Insurance analytics, ML, BI and AI 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. This can enable expedited claims management and processing to help the insured get themselves back on their feet.
Building A Future-Proof Insurance Data Management Strategy Thanks to sensors, mobile technology, ML and AI, the amount of rich data about consumer and business activities continues to increase. To have effective data management to support expedited claims management and processing, insurers should look to these operational best practices: Standardize Data Collection Processes And Terminology A common disconnect within insurers is that the same type of information is coded differently across multiple departments. If one department creates its own unique nomenclature for entering new customer data and another department does the same, there could be years' worth of data that focuses on the same category.
Still, the larger strategic insights are left unidentified in disjointed processes. Insurance senior leaders must be a driving force to standardize common data collection and entry procedures. Consistent Employee Training There is no doubt that most new employees receive training in the core functions of their job.
For some businesses, this is often the only chance to introduce key operational standards. To ensure proper data management, training on data collection, entry and analysis needs to be an integral part of quarterly or semiannual performance reviews. By developing a frequent training and evaluation cadence, insurers can better prevent a year or more of data management issues from building up.
These two crucial steps can help enable insurers to be nimble in response to disasters. The Importance Of Cyber Risk Management Following a disaster, insurers may become so focused on processing claims and managing fraud that they inadvertently overlook cyber risks within their systems and client interactions. Cybercriminals, also affected by disasters, often exploit these situations by creating sophisticated schemes to target insurers working to support their communities.
From a cybersecurity perspective, effective data management also means safeguarding databases and carefully screening any external or third-party information that enters the system. Insurers can enhance their services by offering tailored insurance solutions or advice, helping customers stay alert to cybercriminals who may impersonate local agents to exploit those in the process of recovery. Insurers and the insured need to look to cyber insurance in disaster-prone areas, and the data fueling these cyber insurance products is of the utmost importance.
There are also resources from the government and private sector vendors for how consumers can beware of cybercrime after a disaster. In Conclusion It would have been more exciting to write my last article of this year about the exciting bells and whistles of emerging technology. However, I believe the insurance industry needs to have a closer look at how they can get ahead of 2025 to better protect their businesses and the insured.
The foundation of a stronger strategy for predicting and responding to disasters is clean, organized data with modern insurance analytics tools that use ML, BI and AI with the right focus. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?.
