The digitization of financial documents is an important task for financial institutions like banks as well as individual banking customers and businesses.
Banks must convert physical customer records into a manipulatable digital form, and the customer and business bank customers must extract data from bank statements and other such documents for other financial processing operations. Optical Character Recognition (OCR) tools are useful for the extraction of data from bank statements and other banking documents.
Let us see what OCR is and how it is used in the banking sector, with a particular focus on how it can help customers and organizations extract data from bank statements. The various areas of application of bank statements OCR are also discussed.
What is OCR?
Optical character recognition or OCR, in its simplistic form, is a software that extracts data from scanned documents, camera images and image-only pdfs, for subsequent processing. Letters in images are picked out preferentially by the software and converted to digital data that can be further manipulated.
In the basic version of OCR, all text present in the parent document is extracted with no differentiation by relevance or importance. Subsequent extraction of required information from the ensemble entails human effort.
The next generation of OCR – zonal OCR – extracts specific data present in specific zones or areas of the documents, in accordance with pre-set rules.
In recent times, OCR tools such as Nanonets are equipped with AI and ML capabilities and can intelligently convert text into categorized data and check for errors that may occur during the conversion.
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Where is OCR used in the banking sector?
The use of OCR in the banking sector falls under two categories – usage by banks and usage by customers and other business entities. Banks use OCR for converting customer information into digital records, signature comparison, customer onboarding, etc.
In the rest of the article, we will see how OCR can be used by the customer and other non-bank enterprises, especially to extract data from bank statements.
All bank account holders, be it individuals or organizations, handle bank statements. A bank or account statement is a document issued by a bank to the customer, describing the activities in a depositor’s bank account during a specific period. OCR-driven extraction of data from bank statements helps account holders helps the customer monitor account balance, track fees and interest and detect identity fraud. Bank statement extracts are also important for tax computation and filing.
For corporations, the extraction of data from bank statements helps monitor the business’s progress and serves as a financial record for tax filing operations. Businesses can use data extracted from bank statements to assess the financial health of the company, total assets, identity liabilities, and list out deductions.
The data extracted from bank statements are used by non-banking organizations for activities such as address verification, identity verification, and credit score assessment of individuals. Bank Statements OCR is useful in the following sectors.
- Healthcare: Bank statements are sought from patients by healthcare systems to process medical bills, and insurance claims and issue credit. Manual entry of data from these statements into the central database is time-consuming and error-prone. Zonal and AI-enabled OCRs can hasten the process and eliminate the occurrence of errors.
- Real estate: The purchase of land and property often requires bank statements of the potential buyer for sales approval. Bank statements are also used as proof of address and identity for buying, selling, renting or leasing property. Bank statement OCRs can help realtors and real estate agents digitize the bank statements of potential clients for faster processing of the purchase/lease/rent process.
- Loans: Processing and approval of loan applications by banks and other financial institutions require bank statements of the applicant. Bank statement OCR is commonly used by these institutions to expedite the loan approval process.
To understand how Bank Statement OCR can be used, it is important to understand the contents of a bank statement.
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The structure and content of bank statements
A bank statement is an overview of all transactions conducted in a particular account in a particular time period. It would necessarily contain the following information
- The account number and details of the account holder
- The period of the statement
- The beginning and ending balances of the account
- Deposits in the form of income, cash deposits, etc.
- Interest earned on the account
- Service fees and penalties charged against the account
- Funds are withdrawn from the account in the form of cash, cheques, etc.
It may also contain other information like account type, and other bank details like bank address, branch name, and so on.
Where and how the above data are placed in a statement varies from bank to bank, but in general, an account statement may be physically divided into three parts:
- The part containing the account holder’s details such as name, address, phone number etc.
- The part contains the account details such as A/C number, account type (e.g. savings versus checking), branch details etc.
- The transaction part includes the date, description and amounts associated with credits, debits, interest earned, and penalties levied.
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The customer may receive bank statements either as paper printouts or as digital documents. Mini statements may be printed from ATMs as well. In the case of hard copy statements, the printed statements must first be scanned. Many scanners come with simple OCR software and can convert the document image into an editable text document. When the statement is received as a digital document, usually as a password protected pdf file, or excel sheet, running it through a zonal OCR can extract relevant and required information and store it in a spreadsheet or database for subsequent processing.
Advanced OCR tools like Nanonets can help you extract data from bank statements into spreadsheet data or CSV files. Nanonets use AI to recognize text, data, tables, graphs and other elements in documents and only extract relevant data to be stored in the format of choice. Nanonets’ PDF scraper OCR is particularly useful for converting bank statements into machine-readable structured data formats such as excel files (CVS, XML, JSON etc.). Such structured data can be conveniently included and processed in automated workflows. Automated processing and management of bank statements can streamline a company’s financial operations and avoid delays or errors.
Bank Statements can be converted to Excel, CSV, JSON or XML output data using Nanonets by the following simple steps:
- Login to Nanonets & select “Create Your Own” to build a custom OCR model
- Upload sample PDF bank statements to serve as a training set for Nanonets’ algorithms
- Annotate the PDF bank statements to train Nanonets’ algorithms to identify the important/relevant data or transactions in the sample bank statements
- Build the custom OCR model – Nanonets leverages deep learning to build various OCR models and tests them against each other to pick the most accurate one
- Test & verify – Add a couple of real bank statements to check whether the custom OCR model works well
- Export – If the transactions/data have been recognized, extracted and presented correctly, then export the file – download the data extracted from the PDF as an Excel, CVS, JSON or XML output
Nanonets offers the following benefits in your conversion of bank statements into digital formats:
- Flexibility: Nanonets’ deep learning algorithms can easily handle handwritten text, multiple languages, images with low resolution, images with new or cursive fonts and varying sizes, images with shadowy text, tilted text, random unstructured text, image noise, blurred images and many more common data constraints.
- Customizability: The use of proprietary/custom data to train Nanonets’ OCR models helps meet specific business requirements. Bank statement formats differ based on the bank and the type of account.
- The ability to train OCR models to recognize various formats is ideal for organizations with different kinds of accounts in multiple banks.
- Adaptability to changes: The possibility to easily re-train existing models with new data allows Nanonets’ OCR models to adapt to unforeseen changes.
- Changing bank document formats or new data capture requirements can thus be easily handled.
- Detection of tables: Automatic detection of tables including structured row-column information is particularly useful for bank statement digitization.
- Nanonets offers the facility to export tables to multiple formats like CSV, Excel, & JSON.
- No post-processing is needed: the extraction of relevant data and their automatic sorting into intelligently structured fields minimizes manual post-processing.
- Works with non-English or multiple languages. This feature is important for multinational operators who work across national borders.
- Ease of use, batch processing of multiple documents and seamless 2-way integration with multiple accounting software.
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Bank Statement OCR can extract relevant elements from bank statements and store them in a logical manner for future processing. AI-enabled OCR tools like Nanonets can help banks, individuals and all organizations that deal with bank statements by enhancing service delivery quality, providing access to error-free critical financial data, and allowing periodic assessment of progress and financial wellbeing.
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