The use of generative artificial intelligence in claims management processes can improve the efficiency and quality of the entire process, with advantages for the company, in terms of productivity and anti-fraud, and for the customer. Likewise, the spread of its use can hide new threats, for which it is necessary to foresee the adoption of new control actions
Insurance companies have traditionally relied on human expertise and manual processes to handle claims. But the advent of advanced technologies, particularly generative AI, has opened up new possibilities to transform and streamline the claims process and the claims management chain as a whole. GenAI, combined with machine learning, natural language processing and other technologies, promises to improve accuracy, efficiency and customer satisfaction. While it is too early to assess the full potential of GenAI to reduce costs in the claims arena, there is significant interest from nearly all large insurers as recent analysis suggests productivity gains of 20%-30%.
IMPROVEMENTS FROM THE EXPANDED USE OF GENAI
Current experiments with GenAI promise significant benefits for insurers and their customers. The technology shows particular benefits in several areas, which we explore below.
Accurate assessment of losses
Historically, there have been significant differences in individual performance among adjusters, in terms of their ability to correctly determine the extent of damage or estimate repair costs.
Cumulative experience, employee tenure and commitment, and concurrent workload can influence the effectiveness of claims handlers, as well as their attitude if a claim is filed.
Combined with image recognition and computer vision technologies, GenAI can assess losses with unprecedented accuracy. Adjusters can also use GenAI to analyze external data sources, such as past court decisions, to gather evidence to support a possible negotiation. Additionally, GenAI can help you sift through complex terms and conditions typically found in commercial policies and quickly assess the validity of your claims coverage.
Fraud Detection and Prevention
GenAI gives insurers the ability to analyze large amounts of data from multiple sources, including customer profiles, historical claims data, and external databases. This allows them to accurately assess risk factors and make more accurate decisions about claim admissibility. GenAI’s advanced algorithms can quickly assess the likelihood of fraud and flag suspicious claims for further investigation, allowing insurers to allocate resources more effectively and reduce losses.
Efficient Claims Processing
Claims processing has traditionally been a time-consuming and resource-intensive activity. GenAI can automate several steps in the claims journey, significantly reducing the time and effort required. Typical use cases include preparing standard mailings to claimants and preparing engagement letters to external service providers. This can enrich the role of adjusters and loss adjusters by freeing them up from low-value-added tasks to focus on higher-value activities.
With its natural language processing capabilities, GenAI can analyze claims documentation and extract relevant information, such as policy details, accident descriptions, and supporting documents. This automation leads to faster claims processing, enabling insurers to provide faster resolutions to policyholders.
Customer Service
The customer journey is increasingly an omnichannel experience, with a significant portion of remote interaction directly with the insurance company. This begins with the first claim notification and increases in the later stages of the claim. GenAI virtual assistants have the potential to revolutionize such customer interactions. They can improve customer satisfaction by reducing wait times and providing 24/7 support, ultimately improving the overall policyholder experience.
Intelligent chatbots or voice-bots powered by GenAI provide policyholders with instant access to information and support. Customers can interact with a virtual assistant via websites, mobile apps and messaging platforms; the assistant can offer personalized support by understanding customer questions, providing relevant information about claims status and providing coverage details. It can also guide customers through the claims process, offering step-by-step instructions and gathering the necessary information for a seamless experience.
Data-Driven Business Insights
GenAI can transform the massive amounts of data generated during the claims process into valuable insights. Insurers can identify trends, optimize processes, and make better, data-driven decisions when they bring unstructured data into a usable form that integrates with their core platforms and subsystems. Additionally, by analyzing customer data with GenAI, insurers can identify patterns and preferences, enabling them to provide policyholders with tailored communications and a hyper-personalized experience throughout the claims process.
GenAI’s ability to recognize patterns and correlations in claims documentation, such as adjuster reports, can also help insurers identify areas of risk concentration and improve the feedback loop for underwriting and product design.
RISKS AND IMPLICATIONS OF LARGE-SCALE ADOPTION
Like any technology, generative AI is not a panacea: it carries risks, both external and internal, and requires a holistic and thoughtful approach to implementation that combines the power of GenAI with the machine learning tools that many insurers already have in place. To minimize these risks, insurers must work in particular along certain lines, described below.
Monitor the Potential Increase in Fraud Risks
While GenAI can help insurers detect and prevent fraud, it can also enable it. Fraudulent claims can now be enhanced and embellished in new and compelling ways with GenAI, including generating fake images and creating personas that act human-like.
This requires insurers to recognize the risk of such sophisticated false claims and strengthen their defenses with a combination of robust data analytics, behavioral analytics, collaborative data sharing, human expertise, and customer education. Insurers could also incorporate additional layers of verification into the claims process, such as the use of biometrics, geolocation data, and image recognition to validate the authenticity of claims.
Protecting Customer Data
GenAI requires responsible adoption. Insurers must be aware of the regulatory and reputational risks associated with the potential disclosure of their customers’ sensitive personal data. Companies adopting GenAI solutions must create a closed environment to protect data. They must also avoid model outputs that could reinforce biases against certain customer groups, such as biases based on race, gender, country of origin, religion, or age.
Preserve Organizational Intelligence
In the medium to long term, insurers should not use generative AI as a substitute for organizational IQ. Instead, the right GenAI architecture should ensure the continuous development of employees’ professional skills by improving human-machine interaction and enabling co-design of solutions. GenAI should accelerate the acquisition and absorption of experience by claims handlers, not hinder or replace it.
The integration of GenAI into the insurance claims management process marks a significant step forward in the industry’s digital transformation journey. From streamlining claims processing to improving the accuracy of assessments, from providing personalized customer experiences to detecting fraud, GenAI offers insurers immense potential to improve efficiency, reduce costs and improve customer satisfaction. As GenAI continues to evolve, so will the competitive advantages of insurers that adopt this technology. But beware: from a technological perspective, success begins with data quality, and GenAI is not immune to the old adage “garbage in, garbage out.”
