Three ways a data consortium helps insurers fill the data gap

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Loss history is one of the most important data points insurers can leverage to assess risk. But what happens when loss history isn’t available? How can insurers accurately select and price risks? For the small-commercial market—where this challenge is frequently encountered—an in-depth data set can help.

While insurers are known for collecting extensive information about the businesses they protect, there often isn’t enough variety in an insurer’s siloed data set to build a robust and highly accurate predictive model for risk selection and pricing. However, pooling the same type of transactional data from multiple insurers such as recent premium, policy, billing, claims, and audits can help paint a much clearer picture. This is the principle behind a data consortium.

Combined with an insurer’s own data set, a consortium containing anonymized transaction data can help build customized predictive models that achieve much higher lift. By leveraging both structured data (numbers, text, etc.) and unstructured data (social media content, wearable technology reports, etc.), predictive analytics can provide insights that are otherwise out of reach for insurance carriers.

1. Doing More with Less
Today, consumers are increasingly mindful of the data they share with companies. The insurance industry must now find ways to assess risk and provide quotes with less information. Insurers can solve this issue by leveraging the shared data from a consortium. This offers individual carriers the ability to perform thorough risk assessments without having to collect as much data on their own. And, with data from more sources, insights can be used to help streamline operations and create more efficient processes from quote to bind. In this case, access to more data allows carriers to improve both the customer and agent experiences.

2. More Data, Better Customer Experiences
In our on-demand world, customer satisfaction has become paramount. From instant quotes to internet of things (IOT), keeping up with the real-time customer expectations is influencing not only an insurer’s reputation, but also their product road map. The keys to improving product development and customization, as well as personalizing the customer experience (or CX), lie within a data consortium. Today’s social culture means more personalized (unstructured) data. The more personal the data, the better the opportunity for giving customers exactly what they want. From more affordable coverages to specific types of services, carriers will have the data to deliver on their CX initiatives.

3. Simplify the Complex
The ability to mine and analyze vast amounts of data can also ease pain points that insureds may have throughout their journey, such as during the claims process. The policyholder’s claims experience is often considered “the moment of truth,” as it encapsulates the most impactful and crucial engagement with the insurer. Yet, it is also an extremely complex process where the main priority for the insurer and the policyholder are vastly different. The policyholder wants a speedy resolution and payout, while the insurer wants to ensure accuracy and legitimacy, and also mitigate the risk of litigation—all while controlling process costs and providing the policyholder with a great experience. Incorporating data and analytics into the claims process helps satisfy the needs of the insurer and policyholder by dramatically reducing the time it takes to effectively perform these tasks. It can expedite the analysis of claimant information, help assign appropriate adjusters, and more quickly and accurately identify fraudulent or jumper claims.

Bottom line, the more data available to insurers, the better equipped they will be to deliver a fast and modern experience that meets both the expectations of their customers and stakeholders. With the assistance of high-data volume through an extensive and diverse data consortium, insurers large and small can more accurately predict future losses, assess risks, and make customer satisfaction a competitive differentiator.

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Data science Big data Data sharing
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