Intelligent Automation in Insurance 2024: Use Cases & Examples

According to a survey by PwC, 41% of consumers are likely to change their insurers due to a lack of digital capabilities. On the other hand, the insurance industry is lagging behind other industries in the adoption of AI and automation technologies.

The highly unstructured nature of insurance documents and strict regulations could explain the slow adoption of emerging technologies in the industry. Documents come from multiple channels such as emails, PDFs, phone calls, etc., often contain handwriting, and require an understanding of the context.

Leveraging intelligent automation, also called cognitive automation or hyperautomation, which is the combined use of RPA, with AI technologies such as machine learning, NLP, OCR, or computer vision can help insurance companies overcome these challenges. In this research, we’ll explore various use cases and case studies of intelligent automation in the insurance industry.

Use cases

Claims management

Efficient claims processing is one of the most important operations that determine the success of insurance companies. However, claims management is a labor-intensive process consisting of multiple tasks that require manual work. Intelligent bots with RPA, chatbot, and NLP capabilities can streamline claims-related processes:

• First Notice of Loss: FNOL is the first step of claims processing where the policyholder provides the insurer with detailed information about the incident. An intelligent bot can guide the policyholder to submit information, take photos etc. and enter them into the system without human intervention.
• Claims document processing: NLP-powered bots can extract data from paper claim submissions, classify documents, and enter the extracted data into the claims system.
• Fraud prevention: Bots trained on fraud detection algorithms can identify fraudulent claims.

Intelligent automation can help insurance companies to:

• Improve customer satisfaction with rapid claims handling
• Increase employee productivity by reducing the need for manual work
• Decrease fraud-related costs with AI-driven fraud detection

Feel free to check our article on AI-driven claims processing.

Underwriting

Pricing the risk better with efficient underwriting is another factor that can help insurance companies remain competitive. Intelligent automation can improve underwriting-related processes by:

• Processing and extracting relevant information from application documents with document capture technologies.
• Collecting data from internal and external sources and analyzing it to assess the risk profile of new applications.
• Leveraging ML models trained on relevant data to offer a premium.

Automated underwriting can enable insurers to price risk more competitively, improve operational efficiency, and increase customer satisfaction.

For more, feel free to check our articles on the use of AI in insurance underwriting and underwriting automation.

Policy management

The policy must be issued after the underwriting process of new applications has been completed. Existing policyholders may request an update of the information in their policy. At the end of the policy period, policies must be renewed with coverage and premium adjustments. 

Managing all these policy-related processes involves a lot of manual work. Intelligent bots can process and classify documents and reports, extract relevant data, update systems, and inform policyholders with all relevant information.

Regulatory compliance

Insurance companies must comply with strict regulations that can change frequently. Keeping up with these changes manually is prone to errors that can cost the business and the customers. Intelligent bots can automate insurance compliance processes, such as:

• Name screening
• Customer research and validation
• Monitoring regulatory announcements for upcoming changes
• Generating compliance reports

Moreover, by replacing manual data works, intelligent bots can increase data accuracy and eliminate unauthorized access.

You can also check our article on compliance automation.

Case studies

Feel free to read our article on intelligent automation case studies. Some example case studies in insurance include:

Safe-Guard

Problem: Safe-Guard Products provides finance and insurance products and services to the motor vehicle industry to protect consumers. To remain competitive, the company realized the need to move beyond paper-based contract and claims handling processes which take considerable time and effort.

Solution: The company adopted an intelligent automation solution that automates the capture and classification of paper documents, emails, and faxes and sends them to its document management hub. The solution also helps the company analyze these processes and identify the areas that need improvement. It also sends customers regular updates on the status of their pending claims or contract applications.

Result: The company reduced the time to capture documents from 2 hours per day to 10-15 minutes and reduced the time it takes for an employee to handle these documents from 3-5 times to once. These reduced the time to adjudicate a claim by 75%. It also reduced the number of calls from customers checking the status of their application by 25%.¹

For more on intelligent automation

If you want to identify use cases for your business, you can read our comprehensive article on intelligent automation use cases & examples. Also, don’t forget to check our sortable/filterable list of intelligent automation solutions. And if you have other questions, we can help:

Sources

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