Financial institutions like banks are tasked with processing millions of customer documents every year and have to digitize their records for faster storage and retrieval. Optical Character Recognition (OCR) is an emerging technology that helps extract data from pdf/images to convert paper-based documents and pdf documents to Excel, CSV, and other formats. OCR allows banks to quickly scan customer data from PDF documents, convert, and make them searchable with efficient document processing, thus accelerating the rate of approvals for loans and new account applications.
A bank statement is a summary of transactions sent to account holders every month by financial institutions. It gives an overview of credits, debits, charges, and settlements by listing details in a tabular format, letting individuals know how cash flows to and from their accounts. Time periods and dates for all transactions are printed on bank statements, which lenders look at when doing application reviews.
What is a bank statement? In short, it’s a document that summarizes your financials for a given month, page by page.
OCR is defined as Optical Character Recognition and it refers to the technology used for scanning images and converting them into readable formats. In simple terms, it is a form of automated data capture solution where characters from documents are extracted, recognized, and processed for electronic conversions. Characters and letters read using OCR technology are digitized and stored electronically in databases by organizations. These are then processed or converted in other file formats for easy sharing, access, and viewing. Banks use OCR to monitor client spending behaviors, analyze bank statements, and evaluate the creditworthiness of individuals. OCR is used by accounts payable departments to eliminate manual data entry, streamline business operations, and for accelerating both customer onboarding and offboarding processes.
OCR technology in bank statement processing has enabled financial institutions to automate data extraction from account statements and process information more efficiently. Bank statement processing automation involves accurately scanning forms and document images, interpreting them, and validating data to ensure there are no errors or missing values.
Docsumo comes loaded with pre-trained bank statement data extraction API to automatically read forms and extract data from them. Here are the steps involved in bank extract automation and processing using the platform:-
Docsumo’s API will ask you to review and approve your extracted documents after you upload them. If you haven’t processed multiple bank statements already, it is a good practice to review fields until the API yields 99% data accuracy. Docsumo’s API is capable of structuring raw data from unstructured texts and organizes the information.
Common fields Docsumo is able to extract from bank statements:-
If you find any instance of incorrect data extracted from these documents, you have the option to review and update. If there are any missing values, you can edit and add. Once you’re happy with your data extraction, go ahead and click on ‘Approve.’
After reviewing and approving extracted data, you’re all set to download it. Docsumo lets you download the extracted data from bank statements into Excel, CSV, or JSON file formats.
Bank extraction automation software has shown promise to increase business efficiency and make it easier to automate data capture from financial statements. Machine learning algorithms make smart document processing possible. Additionally, OCR APIs are capable of performing intelligent analysis when doing automated data extraction and entry. In simple terms, the larger volumes of bank statements you upload, the better Docsumo’s pre-trained API gets at processing your account statements.
If you’re planning to reduce manual data entry and speed up your document processing, talk to us and let’s figure out how we can help!
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.
An automated invoice processing solution helps businesses streamline their accounts payable operations by automating tasks ranging from data extraction from invoices to data validation, and entering data into a database for further analysis and processing. Processing invoices may be one of the most time consuming tasks, but it can’t be ignored as it is directly linked to the finances of the business. Automation solves this problem by improving turnaround times for invoice processing, and improving the quality of data.
Automated document processing involves capturing components present on a document with the help of softwares. It utilizes technologies like Machine Learning, Computer Vision, Natural Language Processing, and OCR. Automatic processing of documents in an organization helps reduce manual labor, compliance requirements, eliminate challenges, and offers speed to the workflow environment. In this article, we cover different techniques used for document processing along with their pros and cons. This comparison will help you choose the best automated document processing software for your organization.
Accounts Payable (AP) is a current liability representing money owed to customers. Analysing the AP turnover (how long does the organisation take to pay the creditors) regularly can help organisations meet deadlines and avoid delinquencies. Accounts Payable influences a company's financial performance, credit conditions, and capacity to recruit investors and provides essential information about its general financial health. When deciding whether to invest or lend money, investors and lenders use these measurements to evaluate a company's solvency and management consulting procedures. In this article, we discuss how to calculate accounts payable, what to interpret and conclude from it, and the limits it has.