Leveraging AI to Streamline Claims Processing in Life Insurance

As a claims director in the life insurance industry, you definitely know – operational efficiency and customer satisfaction rely heavily on the agility and accuracy of claims processing. With the current increase in data volume and complexity, processing times may shift even further.

Traditionally, life insurance requires a 20-person team to sift through first claim reports, medical documents, and policy terms to analyze and process claims. Since approximately 30% of a worker’s time is dedicated to finding and retrieving the necessary data from various sources, it becomes quite challenging to handle such a vast amount of information with a human expert team only.

Imagine a solution that could automate and optimize your claims processing, reducing the time and effort required to manage countless documents. That’s where artificial intelligence (AI) comes in.

In this article, we’ll explore how AI, particularly large language models (LLMs) like GPT, can drive digital transformation in insurance. We’ll explore the benefits AI brings to the table but also discuss potential concerns new technologies can raise. And to show you a real-life case of successful AI implementation, we’ll take a look at SecureLife, an insurance company that benefits from new technologies in its claims processing system.

So, if you’re ready to discover how AI can transform your insurance industry, keep reading – the future of insurance is here, and it’s powered by AI.

AI Solution in Life Insurance

90% of insurance customers say that claims processing influences their loyalty to their insurer. This means that insurance companies constantly try to find new, more efficient ways to process claims faster and more accurately.

That’s where AI can help. Global use of AI in the insurance market is expected to reach USD 35.77 billion by 2030. This surge isn’t surprising – AI technologies, especially large language models like GPT, can automate the extraction and analysis of key information (e.g., patient information, cause of death, date of policy activation, coverage, etc.) from various documents, healthcare forms, and policies involved in claims processing.

By taking on the analytical work traditionally performed by human teams, AI can assess coverage eligibility and compute compensation amounts, offering a paradigm shift in how claims are processed.

It usually works like this: a team feeds the AI with documents that should be analyzed. Once input is received, the AI will proceed to extract pertinent data points that you choose.

Leveraging natural language processing (NLP) capabilities, the model can interpret the nuanced language of policy documents to ensure accurate eligibility assessment and compensation calculation. Beyond GPT, machine learning algorithms for pattern recognition and predictive analytics can further improve the accuracy and efficiency of these processes.

AI Benefits and Risks

Companies with AI software in their insurance management systems can expect a number of advantages:

• Cost reduction: AI-powered insurance systems can significantly reduce their costs – all due to automating a big part of labor-intensive tasks, lowering operational expenses, and, potentially, premiums for policyholders.
• Increased efficiency: Beyond cost savings, ML and AI technologies can speed up claims assessments, including first notice of loss (FNOL). By analyzing and automating large volumes of data, these technologies help reallocate human resources from repetitive tasks, making the sorting and processing of claim documents, historical information, and images much faster.
• Accurate processing: With human input and continuous training, AI’s learning algorithms can be reached over time, reducing the potential errors and improving accuracy.
• Data-driven insights: With real-time information about the medical conditions or habits of the clients, companies can identify potential roadblocks and predict the risks associated with various factors.
• Fraud detection: With historical claims data, insurers can train AI to spot inconsistencies, unusual claims, and suspicious patterns that might indicate potential fraud to verify claims more in-depth, making the system more reliable.
• Scalability: During high claim periods or disaster events, companies often need to hire extra experts to deal with peak loads. However, AI systems can be easily scaled to handle any amount of data with precision and speed.
  
  

Challenges and Risks of AI Adoption

While AI-powered solutions can be a great choice for companies searching to automate their processes, it’s essential to know the risks AI adoption may pose.

• Data confidentiality: Since AI collects, stores, and processes sensitive personal and medical data, data confidentiality has become one of the most pressing concerns.
  Solution: Insurers must guarantee the ethical collection of clients’ data, as well as take extra steps: implement robust encryption, access controls, and data anonymization techniques and adhere to data protection laws (e.g., GDPR).
• Integration complexity: Incorporating AI into existing systems can be quite complex, requiring substantial time, resources, and expertise to avoid any disruption in current operations.
  Solution: Use a phased integration approach – run a test project to test compatibility and outcomes before fully integrating AI in your system.
• Accuracy: While AI can reduce the number of errors, misinterpretations or inaccuracies may still occur, leading to incorrect claim outcomes.
  Solution: Use a hybrid model, where a small team of human experts reviews AI’s output. This will deliver a safety net for AI’s decisions and facilitate the model’s ongoing training.
• High initial investment: Developing and implementing AI solutions can potentially require a significant investment in technology, talent, and training, which may not be feasible for all organizations.
  Solution: While it may seem like AI solutions require high investment, in the long run, they save companies thousands of dollars. In addition, companies can adopt scalable AI solutions that allow for gradual investment.
• Continuous monitoring: AI models require continuous monitoring and updating to operate effectively and accurately.
  Solution: Perform ongoing monitoring, maintenance, and training of AI systems to ensure they evolve with industry standards and regulatory requirements.
  
  

Implementing AI: Case Study

To better understand how AI can integrate into your insurance system, let’s take a look at SecureLife, a life insurance company. With a number of clients, extensive medical records, and a mix of digital and handwritten documents, SecureLife faced certain operational challenges. Delays in document treatment due to long data extraction, analysis, and verification were just a couple of problems the company was dealing with on a daily basis.

Employing AI has helped SecureLife expedite its claim processes, raise customer satisfaction, and cut down costs. Here’s how the AI was implemented:

1. The company integrated AI software into its insurance management system to digitize and organize all the relevant documents.
2. AI extracted crucial details (such as medical diagnoses, policy terms, and beneficiary information) using advanced data analysis techniques.
3. AI analyzed the claims against the policy’s terms, considering the medical information and legal criteria to ascertain eligibility.
4. After the analysis, AI calculated the appropriate compensation.
5. A human expert reviewed the AI’s conclusion, confirming its accuracy and ensuring not only customer satisfaction but also reinforcing the reliability of the AI system.

Since only 1-2 specialists were required to overlook complex cases and train AI models, SecureLife has managed to reduce repetitive tasks and allow their insurers to focus on more pressing needs – satisfying the clients. This hybrid approach has helped reduce SecureLife processing time from months to minutes and ensure accuracy and transparency.

Conclusion

By automating claims processing, AI in insurance offers helps companies improve accuracy, reduce costs, get rid of repetitive tasks, and, as a result, obtain a faster return on investment. However, the successful integration of AI requires additional monitoring from an expert. By combining both worlds, the companies can mitigate any potential risks and ensure the reliability of the system.

Try the AI software yourself and help your company stand out in a competitive landscape of life insurance.

Contact Marcin Jabłonowski directly and let’s implement an AI solution to transform the way you do business.