AI in Claims Processing: From Bots to Better Business

Artificial intelligence insurance claims and the role of AI in claims processing deserve separate attention within the framework of analyzing the impact of AI on the insurance industry in general. This is because claims processing has its own set of specific challenges and requirements.

Moreover, let's not forget that claims processing is a critical touchpoint in the customer's journey with an insurance company.

This article will highlight how AI works for claims insurance, the benefits of claims automation with AI, the use cases of AI claims, and their limitations.

Benefits of AI for claims processing

There are many benefits to claims automation with AI, but they all lead to the same result – faster solutions, reduced costs and workload, and better customer satisfaction . All of this leads to competitive advantages for an insurance company using claims management automation through AI.

Enhanced speed

Traditional claims processing methods are time-consuming, involving manual data entry and analysis. AI algorithms can quickly process vast amounts of data by using data analytics, reducing the time it takes to process a claim. AI claims processing means quicker resolution, reduced costs, and better customer satisfaction.

Claims validation

AI systems are adept at validating claims by cross-referencing data against predefined criteria and historical data. They can automatically verify the accuracy of claims data, check for inconsistencies, and ensure that the claim complies with policy terms. This reduces human error and provides a more accurate validation process. The result, again, is saved costs for everyone involved.

Reporting assistance

AI can assist in generating detailed and accurate reports. It can analyze data and provide insights that would be time-consuming to extract manually. These reports can be used for internal auditing and compliance and to improve decision-making processes.

AI-driven analytics can identify trends and patterns in claims data. The result is better strategic planning and resource allocation.

Advanced fraud detection

AI algorithms are particularly effective in detecting fraudulent activities in claims. They can analyze patterns and anomalies that might indicate fraudulent behavior. When detecting fraud, AI in insurance claims uses information from various sources. The sources can be external, not just the claimant information and historical data.

Automated claims management can learn from previous fraud cases and adapt to recognize new types of fraud, making them more efficient over time. This not only saves costs associated with fraudulent claims but also helps maintain the claims process's integrity.

Accurate prioritization

AI claims solutions can help prioritize claims based on their complexity and urgency. As a result, more critical or time-sensitive claims are handled first by more highly skilled professionals adept at particular tasks.

Moreover, AI can assess and categorize claims based on various factors, enabling claims handlers to focus their efforts where they are most needed. In the end, productivity increases, while the workload does the opposite, and everyone is satisfied.

Regulatory compliance

AI can help ensure that claims are processed in compliance with regulatory requirements. It can automatically check for compliance and flag any issues. Companies using AI claims management have a reduced risk of regulatory fines and reputational damage.

This is especially important for heavily regulated industries like insurance and surveillance companies with AI capabilities, so it is beneficial for insurance companies to keep up.

Enhanced collaboration between teams

AI insurance claims tools can improve collaboration among teams and departments by providing real-time data and instant access to analytics. This facilitates better coordination and decision-making across the entire organization.

This is crucial for the decentralized workforce of today, where most professionals work remotely, as face-to-face collaboration between people in different departments is often a thing of the past.

When AI in claims automation is necessary

If you are in the insurance business, without a doubt, you need AI in claims processing automation.

AI claims processing helps with so many things, including:

• Handling a high volume of claims
• Faster processing times
• Fraud detection
• Cost reduction
• Data-driven insights
• Customer experience improvement
  

There is no better thing for automated and routine tasks than AI claims management, given its precise ability to analyze large amounts of data. However, there are many instances where it is best not to use AI and rely on humans only, as AI has limitations where human expertise and empathy should come forward.

AI in insurance claims is inappropriate for complex decision-making requiring human judgment to end experience. Consider a healthcare claim involving a patient with a rare, multifaceted medical condition. Let's say the treatment plan proposed is experimental and not widely recognized.

In this case, AI might struggle to accurately assess this claim due to the rarity and complexity of the case. It requires medical expertise and the judgment of actual human professionals to understand the condition's nuances and the plans for the upcoming treatment.

AI also has nothing on humans in situations requiring empathy for human suffering. In fact, we would go as far as to say it is deeply unethical and inhumane to rely on AI claims solutions in sensitive situations. After a catastrophic event like a major hurricane, many homeowners file insurance claims for property damage, and insurance agents should show understanding of the trauma the customers experience.

Finally, claims processing automation is not a solid choice where biased or incomplete data is involved. It takes human experts to recognize data bias. For example, An AI system used in auto insurance claims processing might be trained on data that reflects historical biases, such as higher claim denial rates in a particular area.

If the AI continues to be trained on this data without correction, it could perpetuate these biases, leading to a disproportionate number of claim denials in this area.

How does it work?

So, how does processing AI claims actually work? Before accessing the journey of artificial intelligence insurance claims from start to finish, we should note that they are seamlessly integrated with corporate systems, applications that the customer uses, and relevant third-party sources.

Let's look into the process step-by-step in more detail:

AI for seamless claims management: 5 examples

1. Predictive analytics

Predictive analytics can forecast the likelihood of claims based on customer profiles and historical data. It is used heavily for risk assessment and fraud detection.

Example: Predictive analytics in auto claims by Aviva Canada

Aviva Canada implemented a Vehicle Remedy Tool integrating real-time predictive analytics into the auto claim setup process. This tool, integrated into Aviva's ClaimCenter system, gives adjusters an instant and accurate repair prediction within approximately 5 seconds. This technology has led to a significant reduction in customer cycle time (Source ).

2. Chatbots and virtual assistants

Chatbots and virtual assistants simulate conversations with human users for customer service.

Example: Metromile's AVA chatbot

Metromile, an American car insurance provider, implemented this chatbot to enhance claims processing. AVA is programmed to review claims and verify their legitimacy. It is claimed to proceed 70-80% of claims immediately (Source ).

3. Image and video-recognition

Image and video-recognition technology analyzes and interprets visual data from images and videos.

Example: AI-driven photo estimation tools used by insurance companies like Allstate.

Photo estimation tools allow customers to upload damaged vehicle photos via an app. The artificial intelligence insurance claims technology then analyzes these images to assess the extent of the damage and estimates repair costs to streamline the claims handling process.

4. Natural language processing

NLP is used to interpret and extract relevant information from claim documents and customer communication by manipulating human language.

Example: Milliman's Use of NLP and deep learning in claims.

Milliman actively uses Milliman to interpret unstructured text data, such as adjuster notes (Source ).

5. Fraud detection

Fraud detection technology is the specific technology that uses AI and machine learning to identify patterns and anomalies indicative of fraud.

Example: V2verify's Voice Biometric Technology

V2verify uses voice biometric technology to prevent fraud by authenticating their users based on voice. The company claims it takes about two seconds to accurately detect the customer's voice (Source ).

The future and forecasts

After analyzing many aspects of claims management automation through AI, let's predict how these technologies would evolve.

• Integrated IoT and AI for real-time processing

Example: In auto insurance, telematics devices will increasingly integrate with AI systems. In the event of an accident, data from the vehicle's sensors can be instantly analyzed by AI to assess the damage, determine fault, and initiate the claims process automatically, leading to immediate claims resolution and payment.

• Advanced predictive analytics for personalized insurance

Example: AI will use data from various sources, including social media, IoT devices, and past claims, to create highly personalized insurance profiles. Insurers could offer dynamic pricing models and personalized coverage options based on individual risk assessments.

• Enhanced fraud detection using deep learning

Example: Deep learning models will become more sophisticated in detecting complex fraud schemes. These systems could analyze patterns across a vast array of claims data and other relevant information sources, flagging clear fraudulent claims and identifying subtle, emerging fraud patterns.

• Voice-activated virtual assistants for claim management

Example: Insurers may deploy advanced voice-activated AI assistants that can handle end-to-end claims processes. Claimants could simply narrate their incident to the virtual assistant, which processes the information, files the claim, and even schedules appointments for repairs or assessments.

• AI-driven predictive claims routing

Example: AI systems could automatically route claims to the most appropriate agent or team based on the complexity of the claim, claimant history, and agent expertise. This would optimize processing time and improve customer service.

• Blockchain and AI integration for transparency and efficiency

Example: Blockchain technology combined with AI could revolutionize claims processing by creating transparent, tamper-proof records of claims and policies. This integration can lead to heightened trust, reduced fraud, and faster claims settlement.

• Self-learning AI systems for continuous improvement

Example: AI systems will continually learn from each claim processed, becoming more efficient and accurate. These self-learning systems could adapt to new claims and evolving regulatory landscapes without explicit reprogramming.

• Use of AR and VR for claims assessment

Example: Insurers might use augmented reality (AR) and virtual reality (VR) technologies powered by AI to assess claims. For instance, in property insurance, adjusters could use AR to assess damage remotely, with AI analyzing the visuals in real time to estimate repair costs.

• Emotion AI for enhanced customer interactions

Example: 'Emotion AI' could be used to read and respond to the emotional state of claimants during interactions, ensuring that communication is not just efficient but also empathetic and tailored to the individual's emotional needs.

In addition, companies will find themselves using generative AI more often.

Final thoughts

To conclude, in 2024, an insurance company has all the reasons in the world to use AI for automated claims management. It saves costs, leads to customer satisfaction, enhances reputation, and is a plus for everyone involved.

One important thing to do is to be aware of AI limitations in insurance. Even though insurance claims need AI, experienced human professionals are still as important as ever, especially when dealing with complex claims.

If you are going to develop a claims processing automation solution in 2024, the cost can be as low as $6.000 for a basic chatbot to $50.000 and 6x more for complex AI solutions, like fraud detection software. A team of professional developers at Binariks can assist you with all your AI claims management software needs.