- #Health insurance claims process flow diagram update#
- #Health insurance claims process flow diagram manual#
The first step in Step Functions orchestration is an AWS Fargate ECS task that leverages Python-based image processing libraries to detect the content, validate it, check for malware, and preprocess it.
#Health insurance claims process flow diagram update#
The Lambda function polling the Amazon SQS will fire off an AWS Step Functions orchestration instance and logs into a checkpoint table maintained in Amazon DynamoDB to flag that processing for this record has ”started.” The same Lambda will also update the claims data store with the flag that the video/image data has been uploaded.The user data upload triggers an Amazon S3 event, which pushes the event to Amazon Simple Queue Service (Amazon SQS).API Gateway also makes an internal, immediate second API call to post a JSON object containing the user/policy/claim information to annotate the previously uploaded image/video data with user data.Mobile app calls Amazon API Gateway, which makes a pre-signed post upload to Amazon Simple Storage Service (Amazon S3).Customer uploads the image(s) or video through mobile app.The underlying orchestration of the AWS services to enable these insights for the adjudicator is as follows:įigure 2 – Claims Adjudication Process Technical Architecture
#Health insurance claims process flow diagram manual#
The adjudicator has all the data they need to evaluate and adjudicate the claim and the customer is spared a manual and labor intensive back-and-forth process with the insurer.įigure 1 – Claim Adjuster User Interface with insights on vehicle damage
In the next step, the adjudicator is presented with a damage appraisal that is created by joining this damage anatomy data with external third-party datasets for repair costs. The adjudicator can also see the type of damage and extent of the damage aggregated from all of the uploaded images. In a centralized one-stop user interface, the adjudicator can see all of the images uploaded by the customer along with the claim record, enriched with both policy and vehicle information. Within auto insurance, CIP is able to reduce the elapsed time for damage estimates from two to three weeks down to just a couple of days. Machine Learning-Backed Adjudication in Auto Insurance Part of Accenture’s Cognitive Insurance Platform (CIP), the solution leverages artificial intelligence to examine and analyze the images, documents, and audio and video feeds submitted for a claim. The adjudicator then has access to all of the insights necessary to evaluate the claim at their fingertips, streamlining the adjudication process, reducing administrative costs, and improving the overall customer experience. To address this issue, Accenture and AWS collaborated to build a machine learning-backed claims adjudication solution. Each P&C claim generates hundreds of MBs of unstructured content in the form of pictures, videos, and audio files that an adjudicator typically has to analyze themselves, extending the time it takes to process a claim. In the Property and Casualty (P&C) industry, the frequency and severity of claims have been increasing and while great strides have been made in digitizing the First Notice of Loss (FNOL) experience, the adjudication experience is still largely manual and heavily human dependent. Increased digitization and automation can help deliver this goal. Insurers today are seeking to enable more efficient claims processing to improve the experience for the adjudicator as well as the customer.
The Challenges with Existing Adjudication Processes
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