Key Summary

Prescriptive analytics is an advanced analytical method that combines machine learning, artificial intelligence, and optimization techniques to offer business insights for P&C insurers. It considers historical data, current data, and external data sources, providing insurers with better data to improve their underwriting practices, manage claims more effectively, and enhance customer service, especially in a cadence with other data analytics methods like descriptive, diagnostic, and predictive analytics.

How Does Prescriptive Analytics Work in P&C Insurance?

Prescriptive analytics can influence future outcomes through optimized decision-making. These are decisions backed by specific recommendations that use actionable insights, not subjective estimations. Future outcomes can be improved in many areas of the P&C insurance industry, from the customer experience to informing more accurate pricing. 

Examples include the following:

• An insurer can use prescriptive analytics to optimize its underwriting process by analyzing data from various sources, such as credit scores, driving records, and claims history. By doing so, the insurer can identify profitable policies and leverage metrics to best support policyholders with options.


• In claims management, an insurer can use prescriptive analytics to analyze historical claims data and external data sources such as weather patterns, social media, and news feeds, to identify patterns and predict the likelihood of future claims and possible outcomes. This information can be used to adjust reserves, manage fraud, and reduce claims processing times.

Algorithms and artificial intelligence can be critical prescriptive analytics tools for many business situations; however, each use should continue to be reviewed on an individual basis. 

What Are the Benefits and Challenges of Prescriptive Analytics?

Benefits:

• Better Decision Making: Prescriptive analytics can reduce the need to focus heavily on validating sources and determining bias vs. fact, instead, focusing on data-driven resource projections to support courses of action easily and accurately.


• Increased Efficiency: By using prescriptive analytics, insurers can automate decision-making processes, remove barriers between managed data sets, and more, to ultimately save time and money.


• Improved Customer Service: The ability for insurers to identify trends and patterns quickly and effectively in customer behavior enables them to offer personalized products and services, as well as better methods for supporting supply chain needs and incoming demand.


• Risk Management: Prescriptive analytics can help insurers identify and manage risks with pre-determined probabilities, reducing the likelihood of losses and improving profitability.

Challenges:

• Data Quality: Prescriptive analytics relies on high-quality data. If the data used is inaccurate or incomplete, or the prescriptive models are not properly established, the results may be unreliable.


• Data Privacy: Prescriptive analytics requires access to large amounts of data. Even with professional safeguards in place, privacy concerns may be raised, which could require comprehensive communication to support concerns.


• Prescriptive model adjustment: Prescriptive analytics requires testing and may take time and investment to procure information that meets insurer standards. Insurers may need to focus more resources on prescriptive modeling if refinement takes more time than anticipated.

What Types of Analytical Approaches Are Used in Understanding the Data?

What are the differences between prescriptive, descriptive, diagnostic and predictive analytics? These are the four distinct types of analytical approaches that P&C insurers use to gain insights into their data and use it effectively.

1. Prescriptive Analytics

As mentioned, prescriptive analytics focuses on providing solutions to problems, utilizes advanced algorithms and simulations to predict the outcomes of various decisions, provides recommendations for future actions based on data analysis, and requires the use of historical data to make those predictions. 

This approach is slightly different from the other three types. 

2. Descriptive Analytics

• Focuses on analyzing historical data to provide insights into what has happened in the past


• Provides data-driven insights that can help businesses identify patterns and trends.


• Helps businesses make informed decisions by providing context for current and past events.


• Can be used to monitor and measure business performance.

3. Diagnostic Analytics

• Focuses on identifying the root cause of problems.


• Helps businesses understand why certain events occurred by examining data in detail.


• Often used in conjunction with descriptive analytics to provide a deeper understanding of trends and patterns.


• Learn More: What is Diagnostic Analytics?

4. Predictive Analytics

• Focuses on using statistical algorithms and machine learning techniques to predict future events.


• Helps businesses anticipate future trends and make informed decisions based on those predictions.


• Uses historical data and other variables to create models that can forecast future events.


• Helps businesses make proactive decisions to avoid future problems or capitalize on upcoming opportunities.


• Learn More: What is Predictive Analytics?

5. (Combined) Data Analytics

Prescriptive analytics is often best used in succession with other data analytics types to have solid data and processes in place. Together, each of these types of data analytics feed into better processes and solutions for underwriting, claims management, operational efficiency, and more.

Learn More: What is Data Analytics?

How Do You Use Prescriptive Analytics in P&C Insurance?

Prescriptive analytics is a powerful tool that can be used in various areas of the property and casualty (P&C) insurance industry, including underwriting, claims management, and customer service. Here are some common use cases:

• Underwriting: Prescriptive analytics can help insurers optimize their underwriting practices by analyzing data from multiple sources, such as credit scores, driving records, claims history, and more.


• Claims Management: By analyzing historical claims data and external data sources, such as weather patterns, social media, and news feeds, prescriptive analytics can help inform claims management decisions. Insurers can identify patterns and predict the likelihood of future claims to adjust reserves, manage fraud, and reduce claims processing times.


• Customer Service: Identifying trends and patterns in customer behavior supports direction for better customer experiences. This information, informed through prescriptive analytics, can be used to offer personalized products and services, improve customer retention rates, and reduce customer complaints.


• Risk Management: Through the identification and management of risks using prescriptive analytics, insurers can use data sources like climate risk and historical risk data to identify areas of high risk and take steps to mitigate those risks.


• Pricing: Prescriptive analytics can help insurers price policies more accurately through the combination of data sources like demographics, past claims, and more. Insurers then can identify the best strategies for pricing and premium adjustment.

Overall, prescriptive analytics is a valuable tool for insurers looking to improve their operations with trustworthy, action-oriented data.