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 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 implemented Amazon Textract, Amazon Comprehend (with a custom machine learning classification model), and Amazon 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 workflow

Figure 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.

Architecture diagram

Figure 1. Architecture diagram

Components in the solution

At 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 extraction

First, the process extracts the text from the submitted documents using the following steps:

  1. For each case, the CohnReznick proprietary web application uploads the documents to the Amazon 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.
  2. The uploaded documents Amazon Simple Queue Service (Amazon SQS) queue is configured to receive notifications for all new objects added to the upload bucket. For every new document added to the upload bucket, Amazon S3 sends a notification to the uploaded documents queue.
  3. The text extraction AWS Lambda function runs every 5 minutes to poll the uploaded documents queue for new messages.
  4. For each message in the uploaded documents queue, 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 handling LimitExceededException on StartDocumentAnalysis API call.
  5. After Amazon Textract finishes processing a document, it sends a completion notification to a completed jobs Amazon Simple Notification Service (Amazon SNS) topic.
  6. A process job results Lambda function is subscribed to the completed jobs topic and receives a notification for every completed message sent to the completed jobs topic.
  7. The process job results function then fetches document extraction results from Amazon Textract.
  8. The process job results function stores the document extraction results in the Amazon Textract output bucket.

Documents classification

Next, 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 creation 

The custom classification feature of 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 processing

The automated document classification uses the trained model and the classification consists of the following steps:

  1. The business logic in the process job results Lambda function determines text extraction completion for all documents for each case. It then calls the StartDocumentClassificationJob operation on the custom classifier model to start classifying unlabeled documents.
  2. The document classification results from the custom classifier are returned as a single output.tar.gz file in the comprehend results S3 bucket.
  3. At this point, the check confidence scores Lambda function is invoked, which processes the classification results.
  4. The check confidence scores function reviews the confidence scores of classified documents. The results for documents with high confidence scores are saved to the classification results table in Amazon DynamoDB.

Human review

The documents from the automated classification that have low confidence scores are classified using human review with the following steps:

  1. The check confidence scores Lambda 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.
  2. The check confidence scores Lambda 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.
  3. The results from human review are placed in the A2I results bucket.
  4. The update results Lambda function is invoked to process results from the human review.
  5. Finally, the update results function writes the human review document classification results to the classification results table in DynamoDB.

Additional processes

Documents workflow status capturing

The Lambda functions throughout the workflow update the status of their processing and document/case details in the workflow status table in DynamoDB. The auditor that submitted the case documents will know the status of the workflow of their submitted case using the data in workflow status table.

Search and package creation

When the processing is complete for a case, auditors perform the final review and submit the generated packet for downstream processing.

  1. The web application uses AWS SDK for Java to integrate with the Textract output S3 bucket that has the document extraction results and classification results table in DynamoDB with classification results. This data is used for the search and package creation process.

Purge data process

After the package creation is complete, the auditor can purge all data in the workflow.

  1. Using the AWS SDK, the data is purged from the S3 buckets and DynamoDB tables.

Conclusion

As 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 additional user stories and using the resources on automated document processing.

Categories: Architecture