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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, 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 document processing, businesses have integrated OCR with machine learning and other AI technologies to develop Intelligent Document Processing (IDP) solutions. With the help of AI and ML, the extracted texts of OCR can then be converted into categorized data and checked for errors.
Bank statement OCR is used to extract important information, such as issue date, name of owner, home address of owner, etc., from bank statement and produce structured data that can be directly used by other systems. 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 address.
To assess applicants’ financial capacity, realtors or mortgage lenders often require them to provide their bank statements to make sure that they will have enough money in their accounts to make their monthly payments and also cover a down payment. However, digitizing all items within 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 has trained several data extraction models 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 Form as we call it.
After uploading the master image, you can mark the detection regions to extract important fields such as names, address, and date of issue to create your Form.
Once you have created your bank statement extraction model, you can test it out by uploading sample samples to see if the model has successfully extracted the data that you need.
After you have tested out your Form, 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.
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.
Sign up or contact us to see how you can benefit from FormX.