Tons of financial data is being generated and collected every day which is being manually processed and extracted to organize. This article focuses on one of such applications that may reduce this manual work by processing the data with the use of AI and Computer Vision, and its Optical Character Recognition (OCR). While we accelerate further with this blog we’ll discuss the type of data that is usually being generated in the financial industry and how OCR can facilitate processing with higher efficiency. Also, we’ll dive into most of the common use cases and applications of OCR with its limitations.
So, let’s jump right into it.
Financial records, often known as financial documents, are used to disclose a company's financial information in a graded and standardized format. The example of some of the standardized financial documents are:-
1. Invoices & Purchase Orders
2. Balance Sheet
3. Checks & Bank Statements
4. Profit & Loss Statement
5. Pay Slips
6. Tax Forms
The electronic translation of typed, handwritten, or printed text images into machine-encoded text is known as Optical Character Recognition (OCR). OCR allows a large number of paper-based documents in a variety of languages and formats to be transformed into machine-readable text, which not only simplifies storage but also makes previously inaccessible material available to anybody with a single click.
The process of automating data extraction from financial documents like Invoices and form OCR works in three phases:
To start, the equipment part, which can be any kind of optical scanner, changes over the record's actual shape into a computerized picture. For instance, assuming there's a report on a piece of paper, the equipment part accomplishes delivering an advanced duplicate of that identical archive. During this cycle, the OCR technology must characterize the spaces of interest in the picture. For this situation, the spaces of interest are the ones that contain text, thinking about the unfilled areas as invalid. That cycle is generally alluded to as changing over the picture into foundations (white, clear regions) and characters (obviously dull regions).
Once the backgrounds and characters have been separated, the process of determining the exact contents of the characters from scanned documents begins. Numbers and letters are identified by the dark spots or characters. The study of these features is done in short parts rather than in bulk. Typically, this refers to a single word at a time if the AI successfully understands the language and characters of the text and the content is plain to read. Pattern recognition or feature extraction is the particular approach for character recognition, with the latter being used for the recognition of newer characters. This can be done in two sub-phases as Pattern Recognition and Feature Extraction.
Later each of the characters in a given record is recognized, they are then changed over to an ASCII code that can be put away for additional utilization. Sadly, no framework is idiot-proof, not even the best ones on the planet. That is the reason most OCR frameworks complete a post-handling stage that will twofold really look at the underlying result. For instance, the characters 'O' and '0' can be almost indistinct, particularly when penmanship is involved. That is the thing that makes the post-process stage significant for precision.
The absolute generally prevailing and normal utilization of OCR in the monetary business and financial sector is document scanning, credit card scanning, data entry and many more.
Financial documents can be classified into three types:-
A) Structured data documents
B) Semi-structured data documents
C) Unstructured data documents.
A structured data document is a document that has aspects that can be addressed for successful analysis. It has been structured into a formatted repository, which is commonly referred to as a database. It refers to any data that may be stored in a SQL database in the form of a table with rows and columns. They include relational keys and can be readily mapped into pre-designed fields.
For most structured data documents, the algorithm is divided into three parts:-
Clear and detectable lines are required for successful cell identification. Tables with broken lines, gaps, and holes result in worse recognition, and cells that are only partially encompassed by bylines are frequently missed by the algorithm. Some papers may contain broken lines, which may interfere with data extraction, however, this may also be accomplished using data processing techniques.
Semi-structured data is data that has not been recorded or prepared in traditional ways. Because it lacks a set schema, semi-structured data does not adhere to the format of a tabular data model or relational databases. However, the data is not entirely raw or unstructured; it has certain structural features, such as tags and organizational information, that make it simpler to examine. P&L statements, IRS Forms, Acord Forms, Bank statements, Invoices are some examples of semi-structured documents.
The position of key identifiers and checkboxes on semi-structured forms varies with the data fields. This is a difficulty for template-based OCR software since it may collect inaccurate data that is situated elsewhere on the page.
To discover the 'position information' for a data point, data extraction from semi-structured forms employs the usage of business rules. These criteria are predicated on the notion that the extracted data is always in the same relative position to a defining feature.
Unstructured data/documents are exactly as they sound – information that follows a freestyle design and along these lines no set construction. You would figure unstructured arrangements would be physically filtered however that is simply false. Unstructured information found in agreements, articles, letters, reminders, and more can in any case be caught with the present progressed OCR Capture calculations.
The information extraction (IE) method extracts functionally organized information from unstructured data in the form of entities, relations, objects, and events. The information collected from unstructured data is utilized to prepare data for analysis. As a result, efficient and accurate unstructured data transformation in the IE process increases data analysis.
IE is conducted on unstructured data by combining several NLP-based techniques such as Named entity recognition (NER), Relation extraction (RE), Event extraction (EE), and salient facts extraction. The analysis can be carried out using these standard methods.
While driving the world in such countless different regards, the financial industry is falling behind in the competition to discard paper. That is on the grounds that banks face a one-of-a-kind test: from in-branch applications for records, credits and different administrations, to checks and month to month explanations, quite a bit of their everyday remaining parts buried in paper and manual cycles.
Adding OCR to process the banking and financial documents may lead to reducing the manual work and operational costs with higher efficiency and accuracy. Here are some of the benefits of using the OCR in financial documents:
It saves you retyping time: The best part about having access to OCR software is that it saves you a lot of retyping time earlier spent in manual data entry. Assume you created a text document of an invoice and it was deleted from your computer as a result of an operating system crash or inadvertent deletion.
Simple document editing: OCR also allows you to edit documents that you have already printed or have in hardcoded PDF format. Even if you have printed them in hard copy, there may be a few papers that demand your attention for editing.
Makes digital searching easier: If you have a tendency of keeping scanned papers or invoices on your computer, you should run them via OCR software first. This will not only allow you to alter the content but will also assist you in making the papers searchable.
Apart from having advantages OCR also has some major limitations like:
• OCR text works efficiently with printed text only and not with handwritten text, so it might lead to inaccuracy which is not desirable in finance.
• OCR solutions are highly efficient for good quality of data but if they are fed improper data then this might lead to inefficiency.
• All the documents have to be checked over carefully after processing and should be manually corrected if differences are found.
• OCR systems are not 100% accurate, there are likely to be some mistakes made during the method of data extraction and data processing.
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.
The traditional supply chain management approach relies heavily on manual work and is time-consuming, error-prone, and expensive. As documentation is an important part of the supply chain that consumes considerable efforts of enterprises in the supply chain workflow, it makes sense to automate the process with the help of intelligent document AI software.
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.