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What Is Semi-Structured Data?
Semi-structured data is a form of data that doesn’t conform to the traditional tabular structure of data models, but it has structured properties that make them more commonly used.
October 12, 2023
Bank statement OCR has been incorporated into different workflows to help businesses automate bank statement processing to provide better customer experience, improve operational efficiency, and reduce costs.
Bank statements are often required during a variety of processes. Financial institutions, such as commercial banks, investment banks, insurance companies, etc., have to manually process piles of bank statements for identity verification, address verification, or credit score assessment. However, manual processing can be error-prone, time-consuming, and quite costly especially when they have to process thousands of bank statements on a daily basis.
Many technologies have been applied to help expedite the digitization and processing of bank statements. One of which will be Optical Character Recognition (OCR). In this blog post, we will talk about how OCR, combined with other technologies like machine learning, natural language processing, and large language model, can help financial institutions automate bank statement processing to speed up various application and assessment processes.
Optical character recognition (OCR) is the technology used to convert images with texts into machine-readable or editable formats. In other words, OCR can extract the texts from images as shown below.
The block on the right contains the OCR result generated from the sample bank statement on the left. As you can see, the result isn’t structured in formats like a table or JSON file that can be processed easily by other software. Computers or software will not be able to tell that the statement date is 9/6/2022 and the date due is 10/6/2022 since all the texts are just scrambled together and separated by single spaces. After obtaining the end result from OCR engines, businesses will still have to manually convert it into structured formats, which can be quite time-consuming and inefficient.
To achieve complete automation of bank statement processing, businesses have integrated OCR with machine learning and other AI technologies to develop Intelligent Document Processing (IDP) solutions that are capable of extracting specific information from PDFs or images and convert them into structured format with minimal human intervention. Many still, however, use the term OCR to refer to IDP even though OCR itself is only capable of converting images to texts. The term Bank Statement OCR, therefore, refers to IDP solution that is capable of automatically identifying and extracting relevant information from images of bank statements and return structured data like csv or JSON.
Bank statement OCR is used to extract important information, such as issue date, name of owner, home address of owner, open balance, close balance, details of transactions, etc., from bank statement and return structured data. The question is, what do businesses do with this structured data?
The most common use case of bank statement is address verification during customer onboarding. When opening a bank account, clients will be asked to provide a proof of address to verify where the clients live. Bank statements are among one of the accepted proof of address aside from utility bill, residence permit, and more. To use it as a proof of address, the bank statements have to be recent or under 90 days. With bank statement OCR, businesses can automatically process bank statements to extract the name of the account owner, the address, and issue date to verify their addresses.
To assess applicants’ financial capacity, realtors or mortgage lenders often require them to provide their bank statement for them to make sure that the recent financial picture matches with what the applicants state in the application. However, digitizing the bank statement manually to extract all the past transactions and balances from a bank statement can take quite a while. Bank statement OCR can then easily extract the required information for businesses to easily evaluate the financial stability of the applicants or buyers.
Aside from using bank statement as a proof of address for customer onboarding, insurance companies also ask for bank statement from the applicants since they are relevant to determining whether the applicants have the motives to make a fraudulent claim. However, manual data entry of bank statements can take quite some time but applicants are often in a rush when filing for insurance claims. As a result, more insurance companies are incorporating Intelligent Document Processing into their workflow.
FormX is an Intelligent Document Processing solution that utilizes OCR, ML, NLP, and LLM like GPT-4 to help business automate data extraction from for a variety of documents including bank statement, receipts, IDs, passports, and more. To automate bank statement processing, you can first sign up for a free account and start creating your own extraction model, or Extractor as we call it.
Sign up for a free trial to start setting up your Bank Statement OCR to convert PDFs or images to JSON.
FormX has provided a set of pre-built extractors, such as Government ID / Passport, Invoice, Receipts, Bank Statement, and Bill of Lading. Click on Bank Statement to create your first extractor.
After creating your bank statement extractor, you can select the data fields, or extraction items, that you want the extractor to automatically extract. You can also create some custom extraction items if needed.
Formatter is a tool that allows you to add several steps to automatically modify the extracted data. You can remove certain characters, only keep a certain language, or declare date format when needed.
After setting up your extractor, you can upload bank statement samples to check the extracted result.
After you have tested out your extractor, you can then integrate the it with your software using RESTful API to automate bank statement processing. The images will be sent to the API endpoint "https://worker.formextractorai.com/extract" with the Form ID and Access Token. You will see the extracted information in the API response. The information can be found in the “Extract” tab.
Alternatively, you may also download our desktop app for batch processing. You may find the link to download under the Extract tab.
After downloading the app, enter the Form ID and Access of your Bank Statement Extractor.
You can then drag and drop all your files to process them altogether. FormX will then generate a csv file that can be uploaded to Excel, Google Sheet, or other software for further processing or analysis.
Businesses are under extreme pressure to provide excellent customer services. However, customers are often frustrated by the time it takes for businesses to process their documents.
FormX has trained different document extraction models, such as bank statement, receipts, IDs, etc., to help businesses resolve the document processing bottleneck to increase operational efficiency and provide better customer services.
Contact us to tell us more about how you would like to automate processing of bank statements or other documents and how FormX can help you do that with ease.
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