What is Risk Data?

Risk data in the property and casualty (P&C) insurance industry refers to the information that insurers use to assess the likelihood and potential severity of losses associated with insuring properties. These data sets include historical data on past events, geospatial data on property location, and social data on individual behavior. Additionally, they provide comprehensive hazard risks with granular detail for better data-driven decision-making. By analyzing this data, insurers can develop policies that accurately reflect the risks associated with insuring a particular property, determine appropriate premiums, assess losses, and mitigate risks.

Risk data plays a critical role in the property and casualty (P&C) insurance industry. From assessing risks associated with commercial insurance for buildings and property, to developing insurance policies for homeowners, insurers use risk data as a piece of their advanced analytics strategies.

Roles played by risk data in the P&C insurance sector include the following:

Risk Assessment

The primary role of risk data in the P&C insurance industry is to help insurers assess risk. Insurers use risk data to determine the probability of a loss occurring and the potential severity of that loss. They also use risk data to evaluate the risk factors associated with different properties such as location, construction type, and occupancy. By analyzing this data, insurers can develop policies that accurately reflect the risks associated with insuring a particular property.

Underwriting

Another critical role of risk data in the P&C insurance industry is to aid the underwriting process. Underwriters use risk data to assess the risks associated with insuring a particular property and to determine the premium that should be charged for that property. They also use risk data within underwriting decisions to choose which risks to accept and which to decline.

Claims Management

Risk data is also essential in managing claims. When a policyholder files a claim, insurers use risk data to evaluate the circumstances surrounding the loss and to determine the appropriate amount of compensation. Insurers additionally use risk data to identify fraudulent claims.

Insurance risk data facilitates better decision-making, especially with high-quality risk analytics. HazardHub uses comprehensive risk data sets to produce extensive and impactful risk data that show:

• Fire risk

• Heat risk

• Wind risk

• Water risk

• Manmade risk

The types of risks in the P&C insurance industry and others are often broken into the following key types of risk data:

Historical Data

Historical data is one of the most important types of risk data used in the P&C insurance industry. Insurers use historical data to evaluate the past and predict the probability of a particular event occurring in the future. For example, insurers may use historical data on flooding in a particular area to determine the risk of future flooding.

Geospatial Data

Geospatial data is also important in the P&C insurance industry. Insurers use geospatial data to identify and validate the location of a property and to assess the risks associated with that location. An example of geospatial data can be thought of as risk management by location. P&C insurance companies may determine the risk of a property being affected by a natural disaster such as a hurricane, wildfire, or earthquake.

Social Data

Social data is becoming increasingly important in the P&C insurance industry, especially as the customer experience changes with new technologies and platforms. Insurers may use social data to evaluate the risk associated with insuring certain individuals. Risk assessment may include determining the risk of a policyholder engaging in risky behavior that could lead to a loss based on the individual’s online presence.

Learn More: “What is HazardHub’s Fire Suppression Score?”

The key to harnessing the power of risk data is to use it effectively. From external data sources that need verification, process control, and approval, to automation of ongoing internal data streams, insurance companies can unlock success with a few keys:

• Key #1: Invest in Data Analytics: Data analytics enables insurers to analyze large volumes of data quickly and accurately, providing insights into risk factors and potential losses. Professional analysis of risk data will help insurers understand information and in turn, build better policies, products, and processes.

• Key #2: Ensure Data Quality: The quality of the data used within data analytics is critical in the P&C insurance industry. Insurers need to test and verify that the data they use is accurate, up-to-date, and relevant. Poor quality data can lead to inaccurate risk assessments, which can result in significant losses for insurers.

• Key #3: Commit to Data Integration: Another key factor in harnessing the power of risk data is data integration. Insurers need to ensure that their data is integrated across different systems and platforms. This enables them to access and analyze data more efficiently, which can lead to more accurate risk assessments, underwriting, and claims management.

• Key #4: Be Proactive with Data Strategy: Whether it be weekly, monthly, or annually, P&C insurers should continuously assess the strengths and weaknesses of their data strategies. Evaluate and re-evaluate to place risk data into the best contexts for business and customer satisfaction.