CohnReznick Automates Claim Validation Workflow Using AWS AI Services
AWS Architecture BlogCohnReznick Automates Claim Validation Workflow Using AWS AI Servicesby Rajeswari Malladi, Brendan Byam, Shanthan Kesharaju, and Winn Oo | on23 JUL 2021| inAmazon Augmented AI,Amazon Comprehend,Amazon Textract,Architecture|Permalink|ShareThis post was co-written by Winn Oo and Brendan Byam of CohnReznick and Rajeswari Malladi and Shanthan KesharajuCohnReznick is a leading advisory, assurance, and tax firm serving clients around the world. CohnReznick’s government and public sector practice provides claims audit and verification services for state agencies. This process begins with recipients submitting documentation as proof of their claim expenses. The supporting documentation often contains hundreds of filled-out or scanned (sometimes handwritten) PDFs, MS Word files, Excel spreadsheets, and/or pictures, along with a summary form outlining each of the claimed expenses.Prior to automation with AWS artificial intelligence (AI) services, CohnReznick’s data extraction and validation process was performed manually. Audit professionals had to extract each data point from the submitted documentation, select a population sample for testing, and manually search the documentation for any pages or page sections that validated the information submitted. Validated data points and proof of evidence pages were then packaged into a single document and submitted for claim expense reimbursement.In this blog post, we’ll show you how CohnReznick implementedAmazon Textract,Amazon Comprehend(with acustom machine learning classification model), andAmazon Augmented AI (Amazon A2I). With this solution, CohnReznick automated nearly 40% of the total claim verification process with focus on data extraction and package creation. This resulted in an estimated cost savings of $500k per year for each project and process.Automating document processing workflowFigure 1 shows the newly automated process. Submitted documentation is processed by Amazon Textract, which extracts text from the documents. This text is then submitted to Amazon Comprehend, which employs a custom classification model to classify the documents as claim summaries or evidence documents. All data points are collected from the Amazon Textract output of the claim summary documents. These data points are then validated against the evidence documents.Finally, a population sample of the extracted data points is selected for testing. Rather than auditors manually searching for specific information in the documentation, the automated process conducts the data search, extracts the validated evidence pages from submitted documentation, and generates the audited package, which can then be submitted for reimbursement.Figure 1. Architecture diagramComponents in the solutionAt a high level, the end-to-end process starts with auditors using a proprietary web application to submit the documentation received for each case to the document processing workflow. The workflow includes three stages, as described in the following sections.Text extractionFirst, the process extracts the text from the submitted documents using the following steps:For each case, the CohnReznick proprietary web application uploads the documents to theAmazon Simple Storage Service (Amazon S3)upload bucket. Each file has a unique name, and the files have metadata that associates them with the parent case.Theuploaded documentsAmazon Simple Queue Service (Amazon SQS)queue is configured to receive notifications for all new objects added to theupload bucket. For every new document added to theupload bucket, Amazon S3 sends a notification to theuploaded documents queue.Thetext extractionAWS Lambdafunction runs every 5 minutes to poll theuploaded documentsqueue for new messages.For each message in theuploaded documentsqueue, the text extraction function submits an Amazon Textract job to process the document asynchronously. This continues until it reaches a predefined maximum allowed limit of concurrent jobs for that AWS account. Concurrency control is implemented by handlingLimitExceededException on StartDocumentAnalysisAPI call.After Amazon Textract finishes processing a document, it sends a completion notification to acompleted jobsAmazon Simple Notification Service (Amazon SNS)topic.Aprocess job resultsLambda function is subscribed to thecompleted jobstopic and receives a notification for every completed message sent to thecompleted jobstopic.Theprocess job resultsfunction then fetches document extraction results from Amazon Textract.Theprocess job resultsfunction stores the document extraction results in the Amazon Textractoutput bucket.Documents classificationNext, the process classifies the documents. The submitted claim documents can consist of up to seven supporting document types. The documents need to be classified into the respective categories. They are primarily classified using automation. Any documents classified with a low confidence score are sent to a human review workflow.Classification model creationThecustom classification featureof Amazon Comprehend is used to build a custom model to classify documents into the seven different document types as required by the business process. The model is trained by providing sample data in CSV format. Amazon Comprehend uses multiple algorithms in the training process and picks the model that delivers the highest accuracy for the training data.Classification model invocation and processingThe automated document classification uses the trained model and the classification consists of the following steps:The business logic in theprocess job resultsLambda function determines text extraction completion for all documents for each case. It then calls theStartDocumentClassificationJoboperation on thecustom classifiermodel to start classifying unlabeled documents.The document classification results from thecustom classifierare returned as a single output.tar.gz file in thecomprehend resultsS3 bucket.At this point, thecheck confidence scoresLambda function is invoked, which processes the classification results.Thecheck confidence scoresfunction reviews the confidence scores of classified documents. The results for documents with high confidence scores are saved to theclassification resultstable inAmazon DynamoDB.Human reviewThe documents from the automated classification that have low confidence scores are classified using human review with the following steps:Thecheck confidence scoresLambda function invokes human review with Amazon Augmented AI for documents with low confidence scores. Amazon A2I is a ready-to-use workflow service for human review of machine learning predictions.Thecheck confidence scoresLambda function creates human review tasks for each document with a low confidence score. Humans assigned to the classification jobs log into the human review portal and either approve the classification done by the model or reclassify the text with the right labels.The results from human review are placed in theA2I results bucket.Theupdate resultsLambda function is invoked to process results from the human review.Finally, theupdate resultsfunction writes the human review document classification results to the classification results table in DynamoDB.Additional processesDocuments workflow status capturingThe Lambda functions throughout the workflow update the status of their processing and document/case details in theworkflow statustable in DynamoDB. The auditor that submitted the case documents will know the status of the workflow of their submitted case using the data inworkflow statustable.Search and package creationWhen the processing is complete for a case, auditors perform the final review and submit the generated packet for downstream processing.The web application usesAWS SDK for Javato integrate with theTextract outputS3 bucket that has the document extraction results andclassification resultstable in DynamoDB with classification results. This data is used for the search and package creation process.Purge data processAfter the package creation is complete, the auditor can purge all data in the workflow.Using the AWS SDK, the data is purged from the S3 buckets and DynamoDB tables.ConclusionAs seen in this blog post, Amazon Textract, Amazon Comprehend, and Amazon A2I for human review work together with Amazon S3, DynamoDB, and Lambda services. These services have helped CohnReznick automate nearly 40% of their total claim verification process with focus on data extraction and package creation.You can achieve similar efficiencies and increase scalability by automating your business processes. Get started today by reading additionaluser stories and using the resources on automated document processing.Rajeswari MalladiRajeswari Malladi is a Senior Solutions Architect with AWS working in the North East Greenfield team. She works with customers to build highly scalable, flexible, and resilient cloud architectures that address their business problems and accelerate the adoption of AWS services.Brendan ByamBrendan Byam is a Senior Consultant at CohnReznick. He is responsible for providing clients custom developed solutions. His background includes intelligent process automation, web application development, and data management/analytics.Shanthan KesharajuShanthan Kesharaju is a Senior Product Manager in AWS Kumo organization. Shanthan has over a decade of experience managing diverse teams in both product and engineering. He is an award winning product manager and has built top trending Alexa skills. Shanthan has an MBA in Marketing from Duke University.Winn OoWinn Oo is a Director at CohnReznick. She leads the application development team in the Advisory practice, specializing in data ingestion, conversion, and integration projects as well as custom application development and process automation. Winn has over 20 years of experience in the tech industry working in software development, innovation, and solution architecture.
This post was co-written by Winn Oo and Brendan Byam of CohnReznick and Rajeswari Malladi and Shanthan Kesharaju CohnReznick is a leading advisory, assurance, and tax firm serving clients around the world. CohnReznick’s government and public sector practice provides claims audit and verification services for state agencies. This process begins with recipients submitting documentation as […]
Rajeswari Malladi
Amazon Augmented AI Amazon Comprehend Amazon Textract Architecture