In fact, bank statement processing is still largely done manually in many businesses. This involves employees typing or copying information into spreadsheets or documents, then manually organizing it into their standardized format. It is not just time-consuming, but also highly prone to errors, which can lead to even bigger problems.
This is where the OCR bank statement comes in. OCR is a technology that allows you to quickly collect ready-to-use data from documents, including bank statements, much faster and easier than with manual methods. OCR bank statements serve as a valuable tool for businesses, especially those that work closely with financial data, such as banks, financial services, and insurance companies.
Explore what OCR bank statement can do to streamline financial data extraction and save you a lot of time!
The Definition
OCR, which stands for Optical Character Recognition, is a technology specialized in automated data extraction from images or documents. A bank statement, on the other hand, is an official document issued by a bank that details the transaction history of a bank account.
In conclusion, an OCR bank statement is a process of scanning, identifying, and extracting every key detail from bank statements. With OCR, PDF or scanned images of bank statements can be converted into structured datasets, instantly in less time.
Bagaimana Fintelite Bekerja
The easiest way to understand a new solution is to know how it works. In this section, we will take you step by step through how OCR automatically processes bank statements:
- Bank statement scanning
The process begins with OCR scans pages of bank statements that have been inputted into the system.
- Data extraction and categorization
OCR identifies and extracts each piece of data from bank statements, which is then categorized and organized based on its context and format.
- Data export and integration
For the last step, the structured data result can be exported into various formats, such as Excel, JSON, or CSV, and can also be directly integrated into your existing database if needed.
Data Extracted
OCR can extract various data from a bank statement, and even with OCR tools like Fintelite, you can customize which specific data needs to be extracted. But here are some examples of the information it can capture:
- Account holder’s information: Name, address, and account number.
- Bank information: Bank name, branch location, and routing number
- Transaction details : Date of transaction, description of transaction, transaction type (debit/credit), transaction amount, and reference numbers.
- Deposit and Withdrawal details: Total deposits, total withdrawals, and its description.
- Balance information: Current balance, available balance, and previous balance.
Why It Matters
Manually processing bank statements is not just outdated, it means you are already being left behind. In today’s competitive landscape, staying efficient and agile is more essential for businesses than ever. One way to keep up and stay ahead is using an OCR bank statement, which can significantly streamline operations. OCR automates repetitive tasks, helping businesses save time, reduce costs, and increase efficiency.
For example, in loan application assessments, OCR can help extract key data from bank statements, turning it into processable datasets ready for faster financial analysis and quicker decision-making.
The Solution
Fintelite AI-powered OCR solution designed to help businesses automate data extraction from bank statements and other types of documents. With Fintelite, you can easily parse and capture transactional data. Handling various bank statement formats? Our adaptive learning engine flexibly extracts data from different layouts with high accuracy. Fintelite also includes fraud detection, helping businesses extract data while screening for potential fraud. Jadwalkan Demo to see how Fintelite can make bank statement extraction a breeze.
