The impact of AI on claims processing

The insurance sector has always been a fertile ground for innovation and technology. In recent years, Artificial Intelligence (AI) has emerged as a game-changing force, significantly transforming traditional processes in this industry. Perhaps nowhere is this more evident than in claims processing - a critical workflow that directly influences customer satisfaction and operational efficiency.

AI is expected to see an annual growth rate of 37.3% from 2023 to 2030, continuing to revolutionize various industries, including insurance claims processing​. The global business related to AI in the insurance segment is expected to reach USD 4.5 billion by 2026, growing at a Compound Annual Growth Rate (CAGR) of 24% from USD 800 million in 2018, indicating a substantial increase in AI adoption for claims processing among other applications​.

AI technology has the potential to streamline and automate several steps in the claims process, leading to faster resolution times, reduced costs, and improved accuracy. Here are some of the ways in which AI is making an impact on claims processing.

Streamlining the Claims Process

Traditionally, insurance claims processing has been time-consuming and labor-intensive, requiring human intervention at multiple stages. This often leads to delays, errors, and inefficiencies. Enter AI. With its ability to analyze vast amounts of data accurately and quickly, AI can streamline the claims process, making it faster and more efficient.

Automation of data capturing and First Notice Of Loss (FNOL) using Natural Language Processing (NLP), along with speeding up claims adjustment through computer vision, are among the top ways AI has been utilized to expedite insurance claims processing in 2023. This has resulted in quicker turnaround times, improved accuracy, and reduced costs for insurance companies.

Enhancing Fraud Detection

Annually, insurance fraud costs exceed USD 40 billion in the U.S. alone. In the UK, detected fraud amounts to over USD 1 billion yearly, with undetected fraud adding over USD 2 billion. Overall, fraudulent claims can constitute a significant portion of an insurer's revenue, sometimes as high as 10%​.

The Insurance Fraud Detection Market is projected to expand from USD 5.34 billion in 2023 to USD 16.98 billion by 2028, with a compound annual growth rate (CAGR) of 26.05% during this period. This growth is propelled by the adoption of technologies like AI, machine learning, Big Data, and the Internet of Things (IoT.) Here are three ways AI is revolutionizing fraud detection in the insurance industry:

• Fraud Detection Efficiency: A notable percentage of insurers believe AI will significantly enhance fraud detection. Specifically, 98% of insurers anticipate that fraud detection will be improved by AI and machine learning by 2030​​.
• Usage Increase: A study discovered that 80% of insurance professionals now utilize predictive modeling for fraud detection, which is a substantial increase from the 55% recorded in 2018​.
• Operational Efficiency: AI helps in quickly detecting fraud, which is crucial as a longer detection time can result in higher losses. The use of operational research tools like data mining, machine learning, and deep learning is crucial in addressing fraud detection challenges.

Various technologies are being employed to improve fraud detection. These include Chatbots to expedite claims processing, Computer Vision to evaluate the cost of loss, IoT to notify claims immediately, and Blockchain to prevent double dipping fraud.

​Enhancing Customer Experience

In today's digital age, customers expect quick, seamless, and personalized services. A study revealed that 87% of customers mention that the effectiveness of claims processing influences their decision to renew insurance with the same insurer. This underscores the importance of leveraging AI to improve claims processing as it has a direct impact on customer loyalty.

AI can meet these expectations by offering real-time claim updates and personalized communication. Moreover, AI-powered chatbots can provide 24/7 customer service, answering queries and guiding customers through the claim process. This not only enhances customer satisfaction but also frees up human agents to focus on more complex tasks.

AI streamlines the claims filing process, helps in finding more personalized coverages for insureds, and improves the efficiency of data processing, which in turn enhances the overall customer experience.

The new generation of smart chatbots not only provides immediate assistance but also personalizes the customer experience based on the individual’s history and preferences. For instance, if a customer has a history of filing claims for car accidents, the chatbot can provide them with information about car insurance policies and accident prevention tips​.

Despite the rise of AI, it's noteworthy that 49% of insurance consumers still prefer to speak with a human advisor when filing a claim, indicating that while AI significantly enhances the customer experience, the human element remains crucial in the insurance industry.

Predictive Analytics and Risk Assessment

Another significant impact of AI in claims processing is its use in predictive analytics. By analyzing historical claim data, AI can predict future trends, helping insurers better manage risk and make informed decisions. It can also help in risk assessment by analyzing various factors such as the claimant's history, the type of claim, and the circumstances surrounding the claim.

Predictive analytics is not new to the insurance sector, but its application has been refined over the years. In 2023, the insurance industry is leveraging better data insights for improved pricing and risk selection through predictive analytics. This helps insurers to manage risks better and offer more competitive pricing to their customers​.

Some of the popular use cases for predictive analytics in insurance include:

• New Customer Risk and Fraud prediction
• Predicting Purchase Intent
• Personalizing User Experiences
• Optimizing insurance pricing and product offerings.

In claims management, predictive analytics can be used to segment or triage claims. This helps in prioritizing potentially high-cost claims early in the process for cost containment or fast-tracking low-cost claims for settlement, thus ensuring more efficient claims management and cost savings.

Advanced modeling methods used in predictive analytics solutions aid in the early identification of high-loss claims, dramatically increasing the efficiency and effectiveness of high-value claims personnel. This early identification helps in better risk assessment and more effective claims processing​.

Behavior-Based Risk Assessment and Pricing:

The proliferation of Internet of Things (IoT) devices (estimated to be 15.4 billion in 2023) facilitates behavior-based risk assessment and pricing. Predictive analytics, combined with behavioral data generated by IoT devices and external factors like driving behaviors and neighborhood safety statistics, enables insurers to assess risk and set premium rates more accurately​.

Product Optimization

Insurers are now able to customize policies at an individual level, thanks to the vast amounts of IoT data and advanced automation capabilities. Predictive analytics based on customer behaviors, buying preferences, and pricing sensitivity are used to offer the most attractive and relevant insurance products, thereby improving overall customer satisfaction and business outcomes.

The bottom line

As AI continues to evolve, its impact on claims processing is likely to grow. By streamlining processes, enhancing customer experience, aiding in risk assessment, and tackling fraud, AI is set to revolutionize the insurance industry. However, it's essential to remember that while AI can automate many tasks, the human touch remains crucial, especially when dealing with sensitive issues like claims. Therefore, the future of claims processing may well lie in a hybrid approach that combines the best of AI and human expertise.

The insurance industry is increasingly leaning towards automation, computational accuracy, and data-driven efficiency, showcasing a rise in AI's synergy within claims processing.