Key Summary

Diagnostic analytics in P&C insurance is a type of data analysis that focuses on understanding why an action or pattern happened. The retrospective analysis examines past events to determine the cause and effect of certain outcomes. Diagnostic analytics is used to identify the root cause of problems and to determine what actions can be taken to prevent and/or understand similar issues in the future.

How Does Diagnostic Analytics Work in P&C Insurance?

P&C insurers use diagnostic analytics to analyze why certain items in claims data, underwriting data, and customer interactions occurred. They uncover these patterns and trends by creating statistical methods through strategies like data drilling and data mining to identify the underlying cause of issues. These will eventually be used with other data analytics types like predictive analytics, descriptive analytics, and prescriptive analytics to take appropriate actions that reduce costs, increase efficiency, and improve the customer experience. 

Diagnostic analysis in P&C insurance typically involves four steps. Here are examples of those diagnostic analytics steps in practice:

• Data Collection: The first step in diagnostic analysis is to collect relevant data. This can include data from various sources such as customer interactions, claims data, financial data, and other sources.


• Data Preparation: The next step is to prepare the data for analysis. This can include cleaning the data, removing outliers, finding anomalies, and transforming the data into a format that can be easily analyzed.


• Data Analysis: The third step is to analyze the data to identify patterns, trends, and correlations, aiming to understand why they are happening and what they mean. This can be done using various techniques such as regression analysis, correlation analysis, filtering, and other statistical methods


• Root Cause Analysis: The final step is to identify the root cause of the problem. This involves looking at the data analysis results and identifying the underlying cause of the issue.

What is the Purpose of Diagnostic Analytics?

Naturally, identifying the root cause of problems is done so that appropriate actions can be taken to prevent similar issues in the future. There are also many other purposes of diagnostic analytics.

Identifying specific use cases like a major shift in product use or a significant increase in claims occurred can support insurance innovation strategies, turning an understanding of causal relationships and causation into effective business intelligence capabilities.

What Are the Benefits and Challenges of Diagnostic Analytics?

Benefits

Diagnostic analytics can be structured to define why challenges occurred. Following diagnostic reviews like root cause analysis, regression analysis, and/or correlation analysis, data visualization becomes clear and insurers can set forth strategies to achieve several key performance indicators (KPIs), such as:

• Improving Decision Making: Diagnostic analytics provides insurers with insights that can help them make more informed decisions about their business operations.


• Improving Customer Experience: Diagnostic analytics can be used to identify issues that customers are experiencing and to determine what actions can be taken to improve their experience.


• Reducing Costs: By identifying the root cause of problems, diagnostic analytics can help to reduce costs associated with issues such as claims fraud or errors in underwriting, as well as inform pricing.


• Increasing Efficiency: Diagnostic analytics can also help to increase efficiency by identifying areas where processes can be streamlined and automated.

Challenges

Implementing diagnostic analytics involves significant investment in understanding, time, and resources. While the use of diagnostic tools can enhance insurer goals and support customer satisfaction, there are challenges associated, such as:

• Ensuring Quality Data: The accuracy and completeness of the data used in diagnostic analytics can have a significant impact on the results of the analysis. Information from data sources must be properly developed to ensure machine learning and human intervention can combine to establish quality data.


• Complexity: Diagnostic analytics can be a complex process that requires specialized skills and knowledge to produce and comprehend.


• Initial Cost: Implementing diagnostic analytics can be expensive, and there may be additional costs associated with data storage and analysis tools.

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

Once P&C insurers determine how to set up their diagnostic analysis and understand how they can see the benefits, diagnostic analytics can be used in a variety of ways. Common uses include:

• Claims Analysis: Identify patterns and trends in claims data. These insights into claims help insurers identify fraudulent claims and improve claims processing.


• Policy Analysis: Drill down and uncover the causes of policy metrics. P&C insurers can leverage these analytics to better predict risk and inform billing, underwriting, and customer decisions.


• Billing Analysis: Analyze billing data to identify discrepancies, forecast revenue, and optimize pricing strategies.


• Underwriting Analysis: Diagnostic analytics can be used to analyze underwriting data. Identify areas where risk assessments can be improved and opportunities for process and profit improvement.


• Customer Experience Analysis: Review and inform better customer interactions that meet their expectations. Diagnostic analytics can identify areas where customer service can be improved and identify opportunities to personalize services.


• Usage-Based Insurance Analysis: Analyze telematics data to gain insights into driving behavior and risk. UBI products and processes can be reviewed with diagnostic analytics to which help insurers offer more personalized and cost-effective policies to their customers.


• New and Existing Product Analysis: During product implementation and review of long-standing products, P&C insurers can use diagnostic analytics to evaluate performance, identify areas for improvement, and optimize product offerings based on customer demand and behavior.

Diagnostic analytics is a powerful tool that can provide insurers with insights into their business operations and customer interactions, with the ability to make a positive impact on the industry.