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.
With an automated data extraction solution, loan documents can automatically be processed end-to-end without any human errors and delays. Automation in loan document processing prevents downtimes, eliminates data redundancy, and allows companies to respond faster to client queries. By combining machine learning with deep learning and OCR, companies can eliminate huge costs, derive actionable insights, and streamline loan processing and approvals through efficient data extraction and analysis.
Mortgage lenders receive multiple identity and income verification documents along with different forms from loan applicants in a variety of formats and styles. Traditional OCR solutions fail to extract data from these semi-structured documents and that’s why more and more lenders are adopting intelligent document processing solutions. IDP solutions not only extract data correctly, they are able to validate extracted data against predefined rules in order to improve accuracy.
Intelligent Document Processing is an automation technology that captures information from a myriad of documents and data sources, extract data, and organizes it for further processing. IDP solutions enable businesses to seamlessly integrate with core processes, eliminate manual labour, address challenges faced in reading different document layouts, and meeting legal & compliance requirements. Accurate data is the foundation of every organization, and IDP assists businesses in dealing with the complexity of processing huge volumes of documents, helping them automate manual data entry processes, and move away from traditional semi-automated OCR workflows.