8 Ways Generative AI Is Rapidly Transforming the Claims Landscape

Generative AI is an artificial intelligence (AI) technique that involves creating new content or data using machine learning models.

Unlike discriminative AI, which focuses on identifying and classifying existing data patterns, generative AI seeks to create new content, typically by using deep learning techniques such as neural networks to learn patterns from large datasets and generate new data that follow those patterns.

And this is where it becomes useful for claims and risk management.

Generative AI has the potential to revolutionize claims administration in the next several years, transforming how insurance companies, third-party administrators and medical management firms manage claims processing and settlement. By leveraging the power of machine learning algorithms and natural language processing, generative AI can automate many tedious and time-consuming tasks in the claims process.

These enhancements will result in increased efficiency and accuracy, reduced costs and improved customer satisfaction; they will also help retain and attract new talent to the industry.

Here’s a closer look at some of the ways generative AI will transform the claims landscape.

1) Distilling Documents and Flagging Data Points

Consider the countless hours a workers’ compensation adjuster or nurse spends reading medical documents, identifying changes, planning for what’s next, and documenting what they’ve read.

Natural language processing algorithms can extract information from claim forms and other documents, enabling claims adjusters to quickly evaluate and process claims without spending a significant amount of time poring over paperwork.

Generative AI streamlines the process by summarizing all medical documents into claim notes, extracting keywords or answering specific questions about the document and bringing it to the attention of the adjuster or nurse items that could have been missed.

2) Claims Processing Automation

Generative AI will automate the claims processing workflow by learning from past claims and identifying new ones that match those patterns. The system can then automatically process claims that fall within specific parameters, reducing the workload on claims administrators and accelerating the claims processing time.

Early adopters are automating lower-value medical-only claims, clear liability property damage-only claims, and some first-party collision, comprehensive and property losses.

The future lies in the potential to expand automated claims handling to other exposures with improvement in the experience and outcomes of today.

3) Fraud Detection

Insurance fraud is a significant problem for the industry, with billions of dollars lost every year due to fraudulent claims.

Generative AI will help to identify potential fraud cases by analyzing claim data and identifying patterns and anomalies that may indicate potential fraud or abuse, such as prolonged workers’ compensation claims with little to no improvement, excessive use of medical resources or exaggerated or inconsistent injury complaints.

These patterns can then be flagged for further review and investigation by claims adjusters or fraud investigators, who can work with health care providers and other stakeholders to determine the best course of action.

Concurrently, generative AI can capture information regarding prior events, incidents and pre-existing conditions and outline any intervening accidents introduced into the claim.

4) Customer Service

Chatbots powered by AI algorithms will be able to answer customer queries and provide support throughout the claims process, reducing the need for non-value-added human intervention and improving the overall customer experience.

By providing quick and efficient support, claims administrators will see improved customer satisfaction and loyalty, thus increasing retention rates.

In addition, using generative AI to automate objective tasks will increase the amount of time adjusters spend interfacing directly with injured workers, which will, ultimately, improve outcomes.

5) Process Overview and Answers

Chatbots will also be used to provide an overview of the claims process and the various stakeholders involved within the claim and their responsibilities, an explanation of the benefits available under their coverage or claim, and specific answers to the status of the claim ranging from the payment of an invoice or bill to next scheduled appointments to the evaluation of the claim.

These chatbots use natural language processing to understand and respond to customer queries in a human-like manner, improving customer service and reducing the workload on claims administrators.

6) Risk Assessment and Underwriting

By analyzing large amounts of data and identifying patterns and trends, AI algorithms will help third-party administrators and their customers to better understand risk factors and develop more accurate underwriting models and claim mitigation strategies.

This can lead to more accurate pricing of insurance policies, reducing the risk of losses due to inadequate premiums or overcompensating for risk factors.

7) Claims Comparisons

Generative AI can be trained to analyze a large number of prior claims and identify patterns and trends that may be useful in estimating the outcome of current claims.

Using machine learning algorithms, generative AI has the ability to identify similarities between prior and current claims, taking into account various factors such as the nature of the claim, the severity of the injury, and the geographic location where the incident occurred.

8) Employee Satisfaction

Finally, generative AI will help to mitigate employee turnover, a problem experienced in many industries due to the requirement to handle and complete ongoing, tedious and objective tasks within a short period of time.

The real rub is the lack of technology and automation to remove these stressors, which can be solved in large part by generative AI; providing more time to engage with claimants and injured workers and be more strategic. When greater collaboration occurs, it more closely connects the adjuster, nurse and others to what matters most: the care and wellbeing of the person on the other end of the claim.

Employee turnover also results in reduced experience and supervision.

As baby boomers retire, there is a real loss of institutional and industry knowledge. Generative AI can bridge this gap. With ChatGPT, answers to statutory and regulatory questions, prior claim outcomes and the next steps that may be best to pursue are just a question away.

The Human Factor

While generative AI can be a valuable tool for claims adjusters in estimating the potential outcome of a claim, it must be used in conjunction with human expertise and judgment.

Claims adjusters and other legal professionals should work together with generative AI to ensure that all relevant factors are considered when forecasting claim outcomes. By combining the power of generative AI with human expertise, claims adjusters can make more accurate and informed decisions, leading to better results for the claimant and the insurer.

Generative AI will transform claims administration in the next several years, streamlining processes, reducing costs and improving customer satisfaction. Insurance companies investing in this technology will likely gain a competitive market advantage, because they can process claims faster and more accurately, detect fraud more effectively and provide better customer service.

As such, generative AI will become an integral part of the claims administration process in the coming years. &