Transforming Claim Processing with Autonomous Agents

Introduction

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.

What are AI Agents and how they Transforming claim processing?

AI Agents

AI agents are computer programs that are designed to carry out tasks independently by making decisions based on their environment, input, and some set objectives. Unlike conventional automation systems that strictly follow predefined instructions, AI agents can think, adapt, and act on their own. They are configured to evaluate their surroundings, learn from past experiences, and make decisions to accomplish specific goals. 
AI agents can vary from basic program performing single tasks to sophisticated systems managing complex processes. They excel in unpredictable environments and utilize their learning capabilities to navigate the internet, interact with applications, process enormous amounts of data and engage in transactions along with refining their methods on the feedback.

AI agents in Claim Processing

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.

Key Challenges: AI agents for Rescue

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.

Akira AI’s Multi agent Solution: Agentic System for Claim processing

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.

1. Composite AI Framework

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.

2. Multi-Agent System Overview

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.

• I)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.   

• II)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.  

• III)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.  

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The Multi-Agent System in Action 

1. 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. 

2. 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. 

3.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.

4. Vision Agent's Role in Visual Inspection

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.

5. 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.

6.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. 

7.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 comes out to be Vehicle Back on Road after it has been repaired via the appointment scheduled indicating the completion of claim process and vehicle repair. 

Technological Stack

Our composite AI framework utilize the component from traditional Machine learning to advance Multi agent systems: 

Comparison with Traditional AI Claim Processing Solutions

Benefits of Akira AI’s Solution

I) Faster Claim Settlements: We can dramatically cut down the time it takes to settle claims. By automating routine tasks and processing data in real-time, we can handle claims much faster than traditional methods. This makes customers happier and lets insurance companies handle more claims with the same or fewer resources.  

II) Robustness Accuracy and Stability: The use of specialized controls, knowledge statistics, and verification techniques by AI ensures that each transaction is handled with a degree of accuracy and precision by using these we can reduce the risk of human error will come to it and we will apply domain specific knowledge to each claim. 

III) Improved fraud visibility: Fraudulent claims are a major concern for insurers, causing huge losses every year. Akira AI solutions include advanced fraud detection that analyzes patterns in claims data, identifies anomalies and flags suspicious cases for further investigation.

IV) Seamless Repair Coordination: Our system’s ability to coordinate repairs efficiently ensures that necessary actions are taken promptly, preventing unnecessary delays, and streamlining the repair process.  

Conclusion 

Akira AI’s Autonomous solution for claims processing represents a breakthrough in the insurance industry. As the insurance landscape continues to evolve, Akira AI’s autonomous claims processing solution allows insurers looking to modernize their operations to stay ahead of the competition, reduce costs and deliver a better customer experience.