Insurers are facing some big hurdles these days. Many are still stuck with outdated manual processes that slow everything down, and handling claims can be incredibly resource-intensive. On top of that, customers are expecting quicker approvals and faster service, which only adds to the pressure.
Artificial Intelligence (AI) could be the game-changer here. Imagine if AI could take over those tedious manual tasks, lighten the workload on staff, and speed up the approval process—suddenly, things would be a lot smoother.
According to Gartner, global AI software spending in the insurance market is forecast to increase 17.4% in 2024 to $9.5 billion and reach $15.9 billion by 2027, with a five-year CAGR of 18.2%. This surge in investment highlights the growing recognition of AI’s potential to transform the industry.
In this article, we’ll dive into how PALO IT's AI-powered claim processing tool, ClaimTrackr, is stepping up to these challenges. By harnessing the power of AI, ClaimTrackr is transforming insurance claims processing, making it faster, more efficient, accurate and customer-friendly. Let’s explore how this innovative approach is changing the game.
How AI automates the manual claim processing procedure?
Leveraging machine learning, natural language & Gen AI, ClaimTrackr automates the traditionally manual insurance claim processing procedure.
- Implementation of AI and Generative AI will enhance data analysis and predictive capabilities
- AI will provide deeper insights, improve accuracy, and streamline reporting processes
- Predictive features will enable proactive decision-making based on anticipated impact fluctuations.
A chatbot can be developed using AI to assist the process of claim processing, and approval.
- Real-time Support: Offer an executive summary of the claims, and also provide whether the claims are valid or not.
- Educational Resource: Share knowledge on membership handbooks
How does the AI tool work?
Leveraging the power of artificial intelligence and machine learning, ClaimTrackr automates the traditionally manual insurance claim processing procedure. Here’s a comparison of the time required for each task with and without ClaimTrackr Flow:
What kind of data do we collect?
For this particular insurance workflow automation , we would need the below key inputs for the AI:- Medical Insurance Company’s handbook & necessary documents
- Previous Claim details
- Claimant (Policy Holder) details – Personal, Medical records, and bills (if any)
How ClaimTrackr works behind the scenes?
Step 1: Data Collection and Exploratory Data Analysis
ClaimTrackr initiates the insurance claim processing by automatically collecting the relevant data such as customer records, external data sources, medical records, policyholder information, and government data, ensuring that all information is accurate and up to date. Once the data is validated, ClaimTrackr performs an automated EDA, revealing helpful insights within the gathered data. This step is pivotal in identifying patterns, anomalies, and historical trends that can greatly enhance the overall efficiency of the insurance claim processing procedure.
Step 2: Embeddings Generation
In this stage, textual data is converted into numerical embeddings using advanced techniques. These embeddings capture the semantic relationships within the data, enabling ClaimTrackr to retrieve and analyze information efficiently. The generated embeddings simplify claim information assessment against policy terms and conditions, medical records, and external data to determine claim validity and calculate settlement amounts.
Step 3: Query Execution and Report Generation
Once a claim is prepared for processing, ClaimTrackr utilizes the OpenAI Language Model (LLM) to evaluate the insurance claim status. A detailed report is promptly generated in response to the user’s query, providing essential information about the claim, its assessment, and the proposed settlement. The report generation process is characterized by its high efficiency and consistency, guaranteeing the inclusion of all pertinent information.
Furthermore, with the help of embeddings, the OpenAI LLM is capable of offering deep insights, conducting a thorough review to detect any potential signs of fraud, and providing actionable recommendations for the claim.
Step 4: Parsing and Final Output Generation
After the report is generated by the LLM, ClaimTrackr employs a parsing technique to refine the report and extract useful insights. ClaimTrackr’s role in this phase involves delivering comprehensive, well-organized data that ultimately speeds up the approval process and reduces the time needed for claim settlement.
Delivering Results with ClaimTrackr
Final report is generated with the final verdict whether the Insurance claim was valid or rejected, rejection criteria were claimed amount vs allowed amount, name validations and disease validation under the Exclusion list of the medical handbook.
Are you an insurance business looking to cut manual processes?
