Ok, maybe not robots, but data process automation is growing at an exponential rate. Every day businesses are finding new ways to replace antiquated, time consuming, and inefficient processes with technology-based solutions.  These technologies are in every industry from fast food with automated ordering systems and self-serve kiosks to robot bartenders.  And in the future we may see self-driving cars that will replace taxi drivers, school bus drivers, and semi-truck drivers.  As you can imagine, other businesses like banks, insurance companies, investment firms, and others in the service industry are following suit by looking for new ways to increase their bottom line by using technology to reduce costs.

By now, we have all experienced prefilled form data like credit card information, name, address, and other identifying fields saved in our devices. A similar technology is called data extraction which automatically populates the data you need in areas that you need it, but the data is not saved.  In fact, the data is coming from other documents and traditionally has to be extracted manually by reading and typing it into the new forms or formats. This process can be time consuming, so in order to increase efficiency, organizations are beginning to leverage data extraction technology to help with reviewing, retrieving, editing, keying, transferring, and making decisions on data that would previously require a human touch.

In order to automate those tasks listed above, and almost any other manual process, Optimal Character Recognition or “OCR” is used.  Like the name suggests, this technology is used to read digital data and then convert, input, and transfer that data into other key systems or tools in order to produce work product.  This one piece of technology is the building block for automation in the work place.  Pairing this technology with algorithms and workflows can revolutionize any manual data entry process and go from slow and unproductive to a high-speed and efficient process. 

This automation has great application in the bankruptcy field.  Many common bankruptcy processes are perfect candidates for automation by using AACER data extraction.  Examples include:

• Automation in the bankruptcy filing can by converting a manual form into a mortgage servicer ledger;
• Automating the proof of claim information which protects creditors when an individual(s) file for bankruptcy;
• Automating the payment change notice which ensures these are filed in a timely manner and managed by the individual(s) legal advisors and trustees.

Every process listed above can be automated in some capacity to make it more efficient.  When we discuss automation in the bankruptcy arena, we are talking about technology extracting data from documents then copying and pasting that information in the correct fields. This eliminates the need for teams of people to manually search for the correct documents, open and find the few key pieces of data needed, and then enter it into a new tool or system.

Some of the benefits of using AACER data extraction technology are that:

• It reduces time by automatically filtering through irrelevant documents, deciphering what is relevant, and collecting the data your project requires.  Plus, these documents are now all digital vs. paper.
• It improves the quality of work by using knowledgeable staff who can proof the results of the OCR process against the original document for you prior to receiving the data, which reduces typographical errors that can happen when a human resource is responsible for translating.
• It streamlines processes by eliminating redundancy that is common in manual processes.  It improves communication by sharing data between departments electronically and creates a common path for distinct data elements that can reused multiple times.

If you or your team spends large amounts of time on repetitive tasks like bankruptcy filing plans or proof of claims and thought there must be a better way, there is!