This article on predictive insurance claims litigation analytics was originally published in Carrier Management.
The implementation of predictive analytics is simple and valuable, and it is key to the transformation of claims and legal expenses into valuable assets for insurance companies.
Predictive analytics answer the question “what might happen?”—for example, predictive analytics could provide a range of the number of lawsuits your company might receive next year based on data trends from last year.
Predictive analytics can provide your company with opportunities for revenue growth and expense reduction. The following list demonstrates just some of the ways that insurance companies benefit from using predictive analytics:
- Allocating resources.
- Producing effective and efficient settlement values.
- Recognizing potentially fraudulent claims.
- Promptly identifying potentially high-value losses.
- Managing expenses.
- Analyzing emerging issue trends to help underwriting write more profitable business and quickly stop writing increasingly risky business.
Descriptive and diagnostic analytics have limited upside potential. They focus on the past. Descriptive answers “what happened?” Diagnostic answers “why did that happen?”
Prescriptive analytics can be very valuable, but they are often very expensive—and difficult to obtain and implement. Prescriptive analytics require you to produce a product to capture business intelligence. For example, Progressive’s Snapshot device uses driving statistics to produce a profitable policy premium. Although this is an ideal use of analytics, it is much more complex to implement than the other types of analytics.
Transform Your Claims and Litigation Departments With Predictive Analytics
Just follow these six simple steps to use predictive analytics to transform your claims and litigation departments:
1. Lead. Identify a leader from your claims and litigation team: the “Champion.”
2. Hypothesize. Ask the Champion to list her assumptions of the emerging issues, such as:
- Increases in litigation.
- Increases in litigation expenses.
- Increasingly risky lines of business or locations.
- Increases in targeting by certain plaintiffs’ lawyers.
- Increases in specific types of claims.
Then narrow the hypotheses. Identify the data points needed to evaluate the Champion’s assumptions, such as:
- General information regarding the policyholder and data points captured by the claims system.
- Legal-specific substantive data, including plaintiff and defense law firms; individual attorneys; judges; jurisdictions; dates and descriptions of the major lawsuit events; and the open and close dates of the claim and case.
- Legal-specific financial data, including defense law firm billing; the components of plaintiff’s claimed damages, including economic, noneconomic, fees and costs; and settlement amounts.
3. Discover. First, identify the relevant scope of the data collection, including the different departments or regions involved and the date parameters.
Then narrow the discovery by identifying the data you can collect using existing systems as well as the data that requires manual collection.
Practice note: You will likely want to analyze data that requires manual collection efforts. If the time and resources associated with manual data collection do not outweigh the benefit, reduce your manual data collection to the most necessary data points only.
4. Investigate. Steps involved here include:
- Properly staffing and strategizing the data collection effort.
- Executing the data collection effort.
- Asking the Champion and other stakeholders to list the data analysis approaches for testing their assumptions about the emerging issues.
- Engaging data analysts to examine the assumptions against the data collected.
5. Evaluate. Conduct a meeting between the Champion and the data analysts to evaluate their findings.
6. Produce. Produce predictive models outlining the range of outcomes that could arise based on the analysts’ review of the emerging issues and data
Putting It Together: Key Takeaways
The three transformative tools described in this article and the two earlier parts of this series are tools that you can put into action today to improve your company. All three of these tools—document automation, descriptive analytics and predictive analytics—are simple yet effective. By implementing one or all three of these tools, you will achieve breakthrough results. Further, by implementing one or all three of these tools, you will turn your claims and litigation expenses into valuable assets for your company.
Legal defense costs are inevitable for insurance companies. By using these three simple innovative tools, you can convert those inescapable legal defense costs into assets. Each dollar you spend will be a future investment in your innovative claims department.