Industries today are moving towards an increasingly electronic-based mindset. Paper submissions, however, continue to make up a considerable amount of incoming claim volume. Many providers are still unable to send claims through electronic channels, therefore, capturing this data can be a lengthy and time-consuming process for many businesses.
Data capturing refers to extracting data from the paperwork and storing that data in an electronic format. It helps to avoid manual workflows. People need to collect documents from various systems, identify the essential data, and then manually enter it into a usable format into another system. The hard part is that the documents containing this data come in a range of formats and file types. The bills of materials, purchase orders, and invoices may be received in emails as PDFs, JPEGs, and Excel sheets or by fax, making it difficult to organize the data. These error-prone workflows choke the firm’s ability to grow faster, which is why data capturing solutions are vital for any business or organization to meet their needs.
OCR, also known as optical character recognition, is a technology that scans each character in your document individually. OCR allows you to extract information from a document and convert it into searchable and editable data. Even if your documents are in a physical form, i.e., a piece of paper, it can be scanned, thereby converting them into the digital format. The extracted information can then be transferred to other systems. OCR is mainly used for invoice processing, document searchability, and sales order processing.
But how does this smart automation actually work? How does the software know what it’s looking at?
Most OCR software use templates so that you can process a large volume of documents through them. These are predefined templates that include check boxes, and option fields so that when you fill a form (template format), the information or data you fill gets inserted into the appropriate fields and is then available to you in an electronic format.
Cognitive solutions combine human capabilities in a single system. They identify patterns and handle massive amounts of data. This means that cognitive data capture systems process documents much the same way as humans do. It involves classifying and extracting data without seeing the document format, examining the layout, finding clues, and ranking to find a document type. Cognitive Data Capture is capable of processing not only huge structured data sets but also unstructured data.
Cognitive data systems can ingest millions of text pages, be it structured or unstructured. These data capture systems are adaptive, interactive, iterative, and contextual. Cognitive technology simulates human thought processes to find solutions to complex problems. They supplement the information to humans for decision making.
Similar to OCR, cognitive data solutions help in reducing a firm’s cost and improve accuracy. Such data capture solutions are useful in overcoming business challenges by imbuing human proficiency and artificial intelligence. Thus, cognitive data solutions simply mean, ‘Human + Machine’ collaboration.
● The major difference between the template-based OCR and Cognitive data system is that the latter allows you to extract and process structured as well as unstructured data. However, for using the template-based OCR for document processing, you need trained template-based data.
● The data structure or format should not digress from the one fixed by you in the template. This is because a slight change in the structure of a document would lead to errors while estimating the results. There are no such rules applicable to cognitive data capture solutions since there is no need for a predefined template.
● Cognitive data capture systems are relatively more scalable.
● Template-based OCR systems reduce the need for human intervention while extracting the data. However, it can take up to 3 hours or more to set up a template, making it time-consuming and slow. On the other hand, cognitive data solutions serve the same purpose by making the document processing fully automated and are relatively faster. The advanced cognitive data solutions comparatively are less time consuming because they use highly trained artificial intelligence.
● Cognitive data systems are more like a blend of artificial intelligence and OCR. On the contrary, template-based OCR systems don’t make use of artificial intelligence for extracting the data.
The role of data capturing software in the realm of logistics is quite prominent. The functioning of complex operations is not possible without back-office processes and plenty of documentation. These documents generally consist of commercial invoices, packing lists, certificates of origin, bill lading, master and house BL.
Having an intelligent data capture software makes the process simple and error-free. Such software provides the business with optical market recognition (OMR), multi-tenancy, intelligent character recognition (ICR), automatic data classification, and optical character recognition (OCR). Instead of entering crucial data manually, cognitive data capture solutions with the help of artificial intelligence meticulously automate the entire process of capturing valuable data.
These systems extract key attributes and tables and then group line items with the product’s description. This extracted data is validated with an ERP system saving plenty of time. Similarly, tax invoice data is extracted from images (these images can be in any format), and values such as tax rates are validated.
Data capture is critical for an insurance company to automate insurance underwriting, claims and compliances. An insurance company needs as much data as possible for the decision making process. Now, requesting a huge amount of information can be time-consuming, especially when the insured doesn’t have information readily available.
Further, while buying an insurance policy, the insurance company asks for personal documents such as identity proof, address proof, medical proofs, etc. There is a significant amount of paperwork involved in selling insurance policies; hence, there is a need for reduced turnaround time and operational costs with great adaptability. That is why cognitive data solutions are considered to be the best fit for insurance firms.
The documents such as tax invoices and bank statements can be extracted in seconds using the data capture software. If you want to extract different values from your, say tax invoice pdf, all you need to do is to select those values and click submit.
The bank statement data can be easily extracted from pdfs to excel or JSON. These systems also verify debit and credit balances, as well as derive key attributes, i.e. an average bank balance. Usually, the accounting data is in the structured form, therefore, OCR solutions would work best.
Data management is extremely important in financial services, as there is a huge amount of data involved which needs to be analyzed for making better investment decisions. Data capture software does the work for you; it analyzes the data and converts financial statements into structured data.
Therefore, highly unstructured annual reports, such as balance sheets, cash flow statements and income statements can be customized as per your requirements. Apart from that, these systems do financial ratio analysis for you, thus, one no longer needs to put in the formulas in the excel sheet to measure the financial stability of a company.
The digital archive is another feature, which makes it possible to convert documents stored in the cloud into searchable databases. In financial services, mostly unstructured data is used, which keeps fluctuating and hence requires frequent variations. Therefore, such firms should adopt cognitive data capture systems.
Many industries, such as call centers, can truly benefit from the infusion of cognitive capabilities. A cognitive system can very quickly take over clerical oriented tasks. The huge problem that many of the banks face is that of the continuously increasing compliance regulation fees, which is again a sweet spot for cognitive capabilities. These systems can trawl millions of documents and be able to reason from them what is the obligation that a bank has to deliver on and then turn it into a policy.
Template-based OCR systems are desirable for companies who use structured data or data that requires minor variations. It’s also suitable for companies that have a few deals and less amount of paperwork. These systems do what they’re asked to do. They are not as intelligent as cognitive data capture systems, which are designed to provide a way out from complex business problems.
“Every company has big data in its future, and every company will eventually be in the data business,” says Thomas H. Davenport
After discussing the two prominent technologies available for data extraction, it’s clear that while both the systems increase productivity and improve accuracy, in the future, it is the system that can process the big data, is more likely to succeed. Our decision making is driven and improved by data capture systems. They recognize patterns and particular clues that can not be seen by the naked eye. The development of skills and knowledge by the use of these systems is our future. Data capture systems guide and improve our decision making. They identify patterns and specific clues that the naked eye cannot see. Our future beholds the advancement of skills and expertise through the use of these systems. The future is competent and having highly intelligent data capture solutions are the need of the hour.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.
As most of an organization's information is available in an unstructured format, processing it requires an automated system that can handle documents with minimum human interaction. OCR is one such technology, but its scope is limited as it requires human interaction and is highly dependent on the layout and structure of the document to be processed.These limitations are overcome by Intelligent Data Extraction.Using artificial intelligence, the Intelligent Data Extraction technology extracts data from documents and transforms it into useful information through the extraction process. It functions as a singular tool for extracting information from any type of document and aids in optimizing company operations.