In fast paced world of insurance, claim processing is one of the critical aspects that affects customer satisfaction and operational efficiency, the rise in generative AI is impacting this process in a positive way. In this blog we will see how AI agents are transforming the claim processing to make it more efficient and secure, giving smooth experience to everyone who is involved in the process. Let's dive into the details.
AI agents in claim processing are LLM (large langague models) powered software systems, having capabilities to take actions. They utilize traditional machine learning techniques, various API, and tools to automate the claim process. AI agents are transforming how insurance claims are processed. These agents are designed to handle tasks that typically require human judgement like analysis of claims and detect frauds at fast pace without bias that can sometimes affect decision making. These agents take on repetitive tasks such as checking and entering the data, which speeds up the process and reduces the chances of error. These AI agents enhance customer service too by providing quick responses to the questions throughout the claim process.
Traditional claims processing relies on the use of manual procedures that are invariably very time-consuming and much error prone. These methods relied on human agents who dealt with a lot of paperwork, communicating with other parties, and making judgment calls that were always subjective.
1. Disjointed Communication: The fragmented communication between the claimants, insurers, repair agencies and stakeholders pose a great hurdle in the process of claim processing. Valuable information can get lost or miscommunicated while creating confusion and delays. This adds to the frustration experienced by everyone involved.
2. Long Processing Times: Traditional claim processing is often slow and painstaking process. As there are multiple steps that require human intervention for approvals, the time it takes from filing of claim to the resolution can stretch for weeks. This delay places additional strain on insurers and repair agencies.
3. Risk of Human Error: Human errors are the most important risk in every manual process. Similarly, there might be an error in data entry, verification of documents, or decision-making that finally results in incorrect settlements, disputes, and even legal troubles while processing a claim. These types of mistakes dent customer satisfaction and may result in financial losses to the insurance company.
4. High Costs: Traditional claim processing methods come with high operational costs because they rely heavily on manual labor for communication and decision-making. This includes costs for staffing, training, and administrative support.
AI agents can address these challenges in several impactful ways:
AI agents enhance the accuracy of the process by handling data with precision and consistency. This reduces the risk of errors in data entry and decision making. Agents automate the manual processes, which lowers the operational costs by decreasing the need for staff. AI agents facilitate interaction between claimants, insurers, and repair agencies by centralizing the communication which reduces the risk of miscommunication and delays.
Fig 1: Akira AI’s Multi agent Solution
At Akira AI, we have developed a powerful Multiagent system specifically designed to tackle these challenges. Central to this solution is our Composite AI framework, integrating traditional machine learning with advanced Agentic AI capabilities.
Akira AI’s claim processing system is built on a unique Composite AI Framework. This combines the power of traditional machine learning with advanced agent-based AI, allowing it to handle diverse types of data—from text and images to structured datasets. This results in more thorough and accurate claim assessments.
Together, traditional machine learning and agentic AI constitute the power behind the implementation of the Composite AI Framework: the former takes up the task of data extraction and pattern recognition, while the latter will be involved in handling complex decision-making. In this way, our system can run itself without human intervention because it will learn, adapt, and improve autonomously.
The framework is designed to handle everything from the text in the claim documents through reports images, and right into structured data emanating from policy records. In bringing all these sources of data together, one would, therefore, be assured that this system analyzes claims from every angle for more accurate and reliable outcomes.
Akira AI's agentic workflow includes unique specialized agents each with precise roles within the claims processing industry These agents work together to speed up claims processing and improve decision-making accuracy spike.
Specialized Agents with Defined Roles: The MAS is built around the idea of role specialization. Each agent has a specific job which is to perform extraction of data to validation of documents to making decisions.
Using Knowledge Graphs for Better Understanding: To make better decisions, the MAS uses knowledge graphs. These graphs map out relationships between different data points within a claim, helping agents understand the full context. For example, they can see how a policyholder is connected to an incident and the relevant coverage terms, leading to more informed decisions.
Real-Time Processing and Decision-Making: Akira AI’s MAS can process data and make decisions in real-time. We can constantly monitor incoming data and adjust the workflows, and the system will react to changes as they happen, which ensures that claims are processed quickly and accurately.
Incident Initiation
The entire process begins with a vehicle collision event. The cameras and the sensors present at the site of the incident capture the event details that traversed. The data is compiled and shared with the insurer.
Incident Reporting Process
After the report has been created related to the event, the claimant files a claim, which triggers our Agentic workflow. Once the data is captured which consists of police reports, claim documents, or captured damage pictures, it is transferred to the intelligent document processing unit, which then extracts the features from the data to be passed on to the multi-agent system.
Multi Agent assessment
The extracted information from the intelligent document processing unit is fed into an Assessment agent which consists of specialized LLM and a Knowledge graph to process the information. This agent categorizes the claims into Simpler Claim and Complex Claim paths.
Simpler claims are directed to derive value and processed without needs of human intervention. On the other hand complex claims require human expertise supported by AI agents' insight. So, these three specialized agents work parallelly to provide comprehensive analysis.
The vision agent comes into play which analyses the data captured of the event. It uses visual inspection like analyzing damaged parts of vehicle.
I) Image Analysis by Convolutional Neural Networks: The Vision Agent uses specially trained LLM that are high-level in the analysis of images in incident reports. In this context, we train them to identify features, like things that might be struck through the vehicle required in the claim's classification process.
II) Extended precision with integration to LMMs: The model of Vision Agent will integrate with LMMs, combining both visual and textual information. This integration with models will enable the Vision Agent to cross-check its image analysis results against any associated textual information, such as incident descriptions and policy details that may be needed for far more accurate and contextualized judgments.
Claim Validator’s Domain-Specific Assessment
I) Rule-Based and AI-Powered Validation Mechanisms: The Claim Validator Agent uses both set rules and agentic AI to check if each claim is valid. It follows policy terms and uses its knowledge graph to spot potential fraud or things that do not add up.
II) Dynamic customization across different insurance sectors: The proof of claim is designed to dynamically customize across different insurance sectors, such as auto, home, or health insurance. This means that he can apply thorough knowledge to each claim, increasing the accuracy of his analysis.
Concierge Agent
This agent is responsible for providing additional support like repair bookings. It interfaces with various related APIs and Insurance agencies to help users provide quick booking slots without delay.
Final Processing
After the agents have completed their processing and claims have been processed, the system employs Agentic AI workflow for automating the routine administrative processes. The outcome is that the vehicle is back on the road after being repaired through the scheduled appointment, indicating the completion of both the claim process and the vehicle repair.
Our composite AI framework utilize the component from traditional Machine learning to advance Multi agent systems:
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Layer |
Component |
Stack |
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Multiagent Layer
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Agents |
For agent development we have been using advanced agents' frameworks like langchain, langraph, Autogen to build SOTA agents |
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RAG (Retrieval Augmented Generation) |
Langchain, llama index frameworks and knowledge Graphs utilized for building RAG pipelines |
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Traditional ML (Machine Learning)
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IDP - OCR |
Integrated Document Processing (IDP) with Optical Character Recognition (OCR) and traditional Named Entity Recognition (NER) |
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Computer Vision Models |
State-of-the-art Convolutional Neural Networks (CNN) were trained on accident-related data |
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Predictive Models |
Traditional Machine Learning models were trained for risk analysis purposes |
|
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Data Layer |
Data pipeline |
We employ industry-leading databases and data pipelines, such as PostgreSQL for structured data and Qdrant for vector data, ensuring a secure and highly scalable solution |
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Backend |
Backend pipelines |
Built using industry best practices to develop secure and scalable APIs |
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Frontend |
User Interface |
Developed using industry best practices to ensure a secure and user-friendly interface
|
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Infrastructure Layer |
Infrastructure |
Utilizes best-in-class infrastructure options, including on-premises, cloud-based, and hybrid solutions |
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Aspect |
Traditional Claim Processing |
AI-Based Claim Processing Solution |
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Degree of Automation |
Automates simple tasks; needs human help |
Fully automates all claims, including complex |
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Error Handling and Adaptation |
Human intervention needed for errors |
Learns and adapts to correct errors automatically |
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Scalability and Efficiency |
Scalability issues with high volumes |
Efficiently handles large volumes without extra resources |
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Processing Speed |
Slower due to manual handling |
Fast processing, reducing cycle times |
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Customer Experience |
Limited interaction and longer response times |
Real-time updates and support enhance experience |
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Data Analysis and Insights |
Basic reporting, limited trend analysis |
Advanced analytics for deeper insights |
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Cost Efficiency |
Higher costs due to manual processes |
Significant cost reduction through automation |
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Compliance Management |
Manual tracking can cause delays |
Automated compliance checks ensure adherence |
Automated Data Entry: AI agents extract and input data from claims forms, minimizing manual errors. This speeds up processing times significantly, improving overall efficiency.
Fraud Detection: AI algorithms analyze claims patterns to identify anomalies and potential fraud. This proactive approach helps protect insurers from significant losses.
Customer Support: Virtual assistants manage customer inquiries in real-time, providing instant updates. This enhances communication and improves overall customer satisfaction.
Claims Triage: Autonomous agents prioritize claims based on urgency and complexity, ensuring critical cases are processed first. This leads to faster resolutions for high-priority claims.
Document Verification: Agentic AI verifies submitted documents against compliance standards, ensuring accuracy. This reduces review times and expedites the claims process.
Claims Forecasting: These agents analyze historical data to predict claim trends and patterns. This helps insurers prepare for fluctuations and manage resources effectively.
Risk Assessment: Autonomous agents evaluate risk profiles of claims, enabling informed decisions on payouts. This improves overall risk management and reduces potential losses.
Workload Management: AI agents are expected to handle 80% of claims processing tasks by 2025. This will significantly reduce the burden on human staff, allowing for better focus on complex issues.
Productivity Boost: These agents can enhance productivity in claims processing by up to 30%. This allows teams to concentrate on more strategic and complex tasks.
Efficiency Gains: Automating routine tasks can lead to a 25% improvement in efficiency. Quicker cycle times enable faster claim resolutions and better customer experiences.
Fraud Loss Reduction: Enhanced fraud detection capabilities can lead to a 10-15% decrease in fraudulent claims. This not only protects revenue but also strengthens insurer credibility.
Operational Cost Savings: Streamlining processes can result in a 20% reduction in operational expenses. This improvement directly impacts the bottom line and enhances financial stability.
Customer Retention Improvement: Improved customer service through agentic AI can boost retention rates by 5-10%. Satisfied customers are more likely to remain loyal, contributing to long-term profitability.
Natural Language Processing (NLP): NLP enhances communication between customers and claims processors through chatbots. This technology allows for seamless interactions and immediate assistance.
Machine Learning: Machine learning algorithms learn from historical claims data to improve accuracy. This enhances both fraud detection and claims assessment processes significantly.
Robotic Process Automation (RPA): RPA automates repetitive tasks such as data entry and document management. This frees human resources for more strategic activities, improving overall workflow.
Predictive Analytics: Predictive analytics utilize historical data to forecast claim trends. This insight aids in better decision-making and resource allocation for insurers.
Image Recognition: Image recognition technology assists in quickly analyzing images related to claims. This speeds up the validation process and enhances overall efficiency.
Blockchain Technology: Blockchain ensures secure and transparent record-keeping for claims. This increases trust among stakeholders and reduces the potential for disputes.
Data Analytics Platforms: These platforms aggregate and analyze vast amounts of data for actionable insights. This drives more effective strategies in claims processing and management.
Increased Personalization: Agentic AI will tailor claims processes to individual customer needs, enhancing user experience. Personalization fosters greater customer satisfaction and loyalty.
Enhanced Predictive Capabilities: Future AI agents will leverage advanced analytics to predict claim outcomes more accurately. This will improve the overall effectiveness of risk assessment.
Integration with IoT: These agents will utilize data from IoT devices to streamline claims related to accidents and damages. This integration will lead to quicker and more accurate claim resolutions.
Continuous Learning: AI systems will evolve through ongoing learning, enhancing their accuracy and efficiency. This adaptability ensures that claims processing remains effective over time.
Regulatory Compliance: Future AI solutions will incorporate real-time compliance checks to meet changing regulations. This proactive approach reduces the risk of non-compliance issues.
Collaborative AI: Autonomous agents will work alongside human agents, enhancing decision-making processes. This collaboration will improve overall outcomes in claims processing and customer interactions.
Enhanced Security Measures: Future AI technologies will integrate advanced cybersecurity protocols to protect sensitive data. This focus on security ensures that customer information remains safe from breaches.
Akira AI’s autonomous claims processing solution is a game-changer for the insurance industry. By embracing this innovative technology, insurers can modernize their operations and stay competitive in a rapidly evolving landscape. The AI-driven approach not only reduces operational costs but also enhances efficiency, allowing for faster claim resolutions. Furthermore, it significantly improves customer experience by providing real-time updates and support. As insurers navigate the challenges of the digital age, adopting Akira AI’s solution positions them to meet customer expectations effectively while driving growth and innovation. This strategic move is essential for any insurer aiming to thrive in today’s market and deliver exceptional service.