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Process mining for insurance firms examples and best practices

Insurance processes are often manual, complex and challenging to manage. As a result, they have been slower to benefit from new technologies such as process mining software. However, new methods have made process intelligence more accessible to insurance companies than conventional process mining. In this article we go through the key use-cases, examples and best practices.

What is process mining?

Process mining is a method of using advanced data analysis to discover, monitor, and improve real-life business processes by extracting data from event logs in information systems. Process mining can be used to identify bottlenecks or inefficiencies in processes, help to better understand how processes are performed, and ultimately improve process performance.

Insurance companies typically use process mining to achieve four key goals:

  1. Process discovery - identify the “as-is” true state of your processes and the variants of your key processes.
  2. Process monitoring - keeping track of conformance to your agreed processes across teams or the organization.
  3. Process reengineering - redesigning your processes to improve efficiency and streamline workflows.
  4. Digital transformation - insurance processes are often manual and time-consuming, process mining can be used to accelerate digitalization.
process mining for insurance claims example
Example of process analysis for insurance claims - Workfellow

Process mining use-cases for insurance companies

Insurance companies can gain a number of concrete business benefits from process mining methods.

1. Accelerate claim processing: insurance companies can use process mining to analyze and monitor the entire claim management lifecycle to identify bottlenecks and inefficiencies. 

2. Monitor and detect fraud: process mining can be used to detect fraudulent activities by analyzing claims data and transaction data from policyholders. 

3. Policy process improvement: process mining can be used to identify areas for improvement in the policy management process, such as identifying opportunities for streamlining the policy approval process. 

4. Streamline underwriting: Process mining can be used to analyze the underwriting process, such as the time it takes to approve applications and the accuracy of risk assessments.

Process mining best practices for insurance companies

Many insurance companies have already piloted or tested process mining solutions with mixed results. Here are a few best practices to ensure success in implementation.

  • Align goals with company strategy. Ensure that process mining project goals are aligned with the overall business objectives and strategy of the organization. 
  • Define success metrics and roadmap. Establish clear and measurable ways to measure success and ensure that the project follows a well-defined roadmap. 
  • Explore and evaluate right software. Utilize the right tools and technologies to ensure that the process mining is conducted in the most effective and efficient manner. 
  • Include relevant stakeholders. Involve the right personnel and stakeholders in the process mining process to ensure that the insights are implemented in the most effective manner. 
  • Develop a robust training program. Invest in training and development to ensure that the personnel involved in the project have the necessary skills and knowledge. 
  • Measure and communicate results clearly. Utilize data visualizations to effectively communicate the insights gathered from the process mining project.
  • Aim for continuous, not immediate improvement. Monitor and evaluate the process mining results on an ongoing basis to ensure that the insights are utilized in the most effective manner. 

Example how process mining is used to detect insurance fraud

One major significant use-case of process mining for insurance companies is fraud detection. Just in the United States, the FBI estimates the cost of insurance fraud to be more than $40 billion annually. Process mining can help insurance companies detect fraud by analyzing the data associated with processes, such as claims and payments. 

How does fraud detection work in practice?

Process mining can identify patterns and anomalies in processes that could indicate fraudulent activity. For example, a process mining system could detect when a claim is submitted multiple times, when a payment is made to an unknown party, or when a process takes longer than usual. By detecting these patterns, insurance companies can take proactive steps to prevent fraud and save money.

In the infographic above you can see an example of audit sampling utilizing process mining software from KPMG. With process mining configured, fraud detection can identify types of transactions to audit based on some common variables, including:

  • Unusual actors or steps in claim process,
  • Lag in transaction throughput time,
  • Unsuccessful steps or loops in a process.

Limitations of process mining for insurance companies

  • Lack of quality data. Process mining techniques are limited to the data that is available, so it may not be able to uncover all the information about a process. For insurance companies this is especially the case when some data systems do not include event log data. 
  • Cost of implementation. Process mining can be time consuming and costly, as it requires the gathering and storing of data over a period of time. Insurance companies that need to integrate various data sources may especially find traditional process mining costly.
  • Automated insights are not perfect. Process mining is not always able to detect process irregularities or fraudulent activities, so it may not be able to identify all potential issues.
  • Lack of agility. Process mining may not be suitable for insurance companies that have complex and dynamic processes, as it is unable to detect changes in the process over time.

Consider an alternative: hybrid process intelligence

If you’ve already tried process mining or you’re still considering your options you may want to explore hybrid process intelligence software. This new approach gives you the level of detail of task mining software while the depth of coverage of process mining software. Not only that, it’s known to be 10x faster in implementation than the well known process mining solution. Book a meeting with our experts to learn more!

Written by

Lari Numminen

Chief Marketing Fellow