RPA and Intelligent Automation for Optical Character Recognition based Business Processes
Many organizations today have well established OCR Processes where accuracy rates are pre-established based on the OCR Toolset deployed and have Standard Operations Procedures defined for the Sorters, Scanner, Verifiers and data-entry operators.
Our purpose here is to address how an Organization can improve Efficiency, Accuracy and/or reduce cost of these Business Processes by using a combination of OCR, RPA and AI Technologies.
Document processing involves following activities:
- Identify: identify what is the type of the document, an image, machine readable text form, handwritten document scanned in the system.
- Classify: based on identification, classify into understandable formats like Invoices, Trade Bills, Time Sheets
- Read: Character Recognize the document
- Interpret: derive conclusions based on text recognized on the document
- Act/Assimilate: perform actions based on the conclusions, like, set up reminders, send notifications, store data into a structured format.
Typical document sets that require OCR vary but here are some that we have seen most commonly used:
- Letters from customers
- Tax Forms
- W-2 Forms
- Legal Bills of Cost
- PositivePay (duplication data-entry)
- Medical Coding/Transcription
- Claim Processing
- KnowledgeBase management
- Policy underwriting and issuance
- Rating Generators
Broadly, we classify these documents into these forms
1. Pre-defined standard formats: These are typically compliance and regulatory documents, like tax forms, w-2 form, etc. Most of the OCR Tools available in the market are able to read these efficiently (98-99%). Digital Process Automation (RPA/IA) tools can be utilized to read, classify and store these into structured data systems seamlessly, more than likely these processes are quite repeatable and easily automated using off the shelf Process Automation Tools.
2. Semi-Structured Enhanced-automation (Natural Language Processing): These types of documents contain free-text in forms of paragraphs and bullet points with data embedded in them. Legal bills of Cost, Medical Coding would essentially fall into these formats. For these documents, most OCR tools extract the text but require a little bit of deductive reasoning from the extracted text to enable it to be stored into structured systems. OCR tools like Ephesoft, Adobe Acrobat DC, Documentum are quite capable at doing these translations. The RPA tools can interact with the APIs provide by these OCR tools and absorb data into your systems. These tools are fairly good with pre-defined data and/or enhanced Automation. For e.g. if we are looking at a letter of explanation from a first-time home buyer explaining source of funds, we found the accuracy at 80-85%. We recommend testing the Tools community edition before buying it for your specific needs.
3. Non-Structured Documents: If you are looking for enhanced readability of the documents without knowing the context, format or considering regional slangs, abbreviated words or short texts or even #tags efficiently, we would recommend developing a solution which would be a combination OCR Tool, Natural Language Processing Engine (NLP) and RPA. Such solutions have a quick build engineering core and give you pretty decent assimilation of data where accuracy rates depend from 75% to 90% depending on the source, for example, for Arabic text and syntactical changes from UAE to Kuwait vary and cause accuracy rate to be low at 78% while when reading English text across various cultures like Australia, England and United States, the accuracy rates are pretty high at 88%, even for reading English from countries like India and South Africa, the accuracy rate was 85%.
Intelligent Automation also comes into play in cases where source of data varies and requires learning the extraction technique from varied sources in varied forms and making smart educated judgements. For example, say a mortgage lender/brokerage company wishes to automate the process of collating and consolidating documentation required for underwriting. Typically, these companies have a document store system which stores all the documents provided by a borrower, but reading these documents and ensuring all sources of funds are well understood and documented require a human. But, this is changing, with intelligent automation solutions, which use a combination of OCR Tools, NLP Engine, Process Automation and Machine Learning to arrive at the ability. For e.g. a typical solution of this for the above problem statement could be outlined in following points:
- First go through the documents attached, identify bank statements (typical need is last 90 days)
- Verify that these are all the bank statements as listed on the Application Form
- Next, identify all sources of funds (deposits into these accounts and who was the sender/depositor)
- Typical sources of funds that you see on a bank statement Salary ACH deposit, Dividend income from investments, stock transactions
- A good intelligent automation solution should be able to correlate the ACH deposits with w-2 forms submitted by the borrower and validate ensuring the numbers and frequency match
- Next, dividend income on the deposits should be correlated with investments declared and the numbers can be correlated with broker statements
- Similarly, credit reports from IRS can be auto read into the system.
This solution enables the company to bring down its underwriting cycle from days to hours and minutes once the necessary documentation is available in the system. Such Machine learning can be implemented using tools like AlchemyAPI, Aylien, Fluxifi, Textalytics and the likes, these can also be used for forensic analysis of data to identify or extract machine readable data.
Such operational automation requires machine learning and ability to build with varied un-defined sources which is a capability you can build within your organization over time. A depiction of activities to solutions: