Bank Statement Extraction: How To Revolutionize Financial Data Management with AlgoDocs AI

Introduction

In today’s world, managing financial data quickly and accurately is very important. One task that takes a lot of time is bank statement extraction. Thanks to technology, especially Optical Character Recognition (OCR), and more advanced technology like Intelligent Document Processing (IDP) and AI, this job has become much easier. This blog will explain what bank statement extraction is, why it’s needed, the challenges it presents, and the best tools for the job. We’ll also discuss how AlgoDocs’ Intelligent Document Processing (IDP) tool can make extracting bank statement data easier and more convenient.

What is a Bank Statement?

A bank statement is a record of all the financial transactions that have occurred in a bank account over a specific period. These bank statements can be personal or corporate financial statements. They provide a detailed overview of all the deposits, withdrawals, and other activities that have impacted the account’s balance. This includes transactions like cash deposits and withdrawals, checks written and cleared, electronic transfers, ATM withdrawals, and even interest earned or fees charged. Essentially, it’s a comprehensive summary of the account’s financial activity, allowing account holders to track their spending, monitor their income, and identify any discrepancies or unauthorized transactions.

What is Bank Statement Data Extraction?

Bank statement data extraction means extracting data from bank statements. These bank statements can be in PDF files, Excel files, or scanned documents. In a bank statement or a standard financial document, you will find data such as transaction details, account balances, dates, amounts, account holder names, tax details, etc. If you want to extract data from a bank statement, you can either choose the manual data extraction method or pick an online financial data extraction tool to get the job done. However, manual data extraction takes a lot of time and is prone to errors, which can create more problems. That’s why businesses these days often use bank statement OCR to make data extraction easy and effective.

Why Bank Statement Extraction is Needed

Many businesses handle a large number of financial receipts, bank statements, and financial records as part of their daily operations. Extracting and organizing this vast amount of data can be a challenging task. Here are some key reasons why bank statement extraction is essential:

• Financial Analysis: Extracting data from bank statements allows businesses to understand their spending patterns, income sources, and overall financial activities. This analysis helps in making informed financial decisions, budgeting, and forecasting future financial needs.
• Bookkeeping and Accounting: Accurate data extraction simplifies the bookkeeping and accounting processes. By having organized and structured financial data, businesses can maintain accurate records and easily reconcile transactions, ensuring that their financial books are always up-to-date.
• Audit and Compliance: Extracting data from bank statements is crucial for meeting legal requirements and ensuring transparency in financial reporting. During audits, having organized data makes it easier to demonstrate compliance with financial regulations and standards, such as those set by tax authorities.
• Fraud Detection: Monitoring financial transactions through data extraction helps in detecting and preventing fraudulent activities. By analyzing bank statement data, businesses can identify suspicious patterns or unauthorized transactions, allowing them to take timely action to mitigate risks.

Which Industries Require Bank Statement Extraction

Many industries require efficient bank statement extraction, including:

• Banking and Finance: This industry relies heavily on bank statement extraction for monitoring transactions, processing loans, and managing accounts. Efficient data extraction helps in maintaining accurate records and ensuring compliance with financial regulations.
• Accounting Firms: Accounting firms use bank statement extraction to prepare financial statements and provide tax services. Accurate data extraction simplifies the accounting process, making it easier to maintain precise financial records and ensure timely tax compliance.
• Legal Firms: Legal firms need bank statement extraction for forensic accounting and legal support. Extracted data can be crucial in investigating financial discrepancies, supporting legal cases, and providing accurate financial evidence in court.
• Healthcare: The healthcare industry uses bank statement extraction for handling payments and processing insurance claims. Efficient data extraction helps in managing patient billing, ensuring accurate insurance claim processing, and maintaining financial transparency.
• E-commerce and Retail: E-commerce and retail businesses rely on bank statement extraction to manage cash flow and vendor payments. Extracted financial data helps in monitoring sales transactions, managing expenses, and ensuring timely payments to suppliers.

Challenges with Manual Bank Statement Extraction

Manually extracting data from bank statements has several problems:

1. Time-Consuming: Entering data manually is time-consuming. Extracting data from each sheet of a bank statement requires significant time for analysis and capture, making the process even more cumbersome.
2. Error-Prone: Human errors can lead to inaccurate records. Manual data extraction is prone to mistakes, as human eyes can’t always interpret everything correctly. This can result in serious consequences, such as data extraction errors, missed valuable data, and other issues.
3. High Costs: Manual data extraction is costly because it requires employing more personnel to extract data from documents. This ultimately increases the operational costs for the company.
4. Scalability Issues: With manual data extraction, you can’t adjust the number of personnel based on requirements. Therefore, scalability options with manual data extraction are very limited.
5. Data Security: When your data is exposed to human intervention, there is a risk that it can be misused for illegal activities. Bank statement data is very sensitive, and exposure to the wrong hands can lead to serious trouble for individuals or organizations.

What is a Bank Statement OCR?

Optical Character Recognition (OCR) is a technology that converts various types of documents, such as scanned papers, PDFs, or images, into editable and searchable data. A bank statement OCR specifically focuses on extracting data from bank statements, turning text in images or PDFs into a structured format.

How an OCR App Extracts Data from Scanned Bank Statements

1. Preprocessing: This step involves enhancing the quality of the scanned document to ensure better text recognition. It may include techniques such as image scaling, noise reduction, and binarization (converting images to black and white). Preprocessing ensures that the document is in the best possible condition for OCR to accurately recognize the text.
2. Text Recognition: During this phase, the OCR app uses complex algorithms to recognize and identify text within the document. This involves detecting characters, words, and sentences from the pre-processed image. The OCR technology can handle various fonts, sizes, and even handwritten text to accurately interpret the content of the bank statement.
3. Data Extraction: Once the text is recognized, the OCR app extracts relevant data from the document. For bank statements, this typically includes transaction details such as amounts, dates, payees, and descriptions. The extraction process ensures that all important financial information is captured from the statement.
4. Post-Processing: In this final step, the extracted data is organized and formatted for easy use and analysis. The data can be exported into various formats like CSV, Excel, or JSON. Post-processing may also involve correcting any misrecognized text, validating the extracted data, and integrating it with other systems for further analysis or reporting.

Best 5 Tools for Bank Statement Extraction

• AlgoDocs: AlgoDocs is an AI powered bank statement data extraction tool which offers High accuracy and faster data extraction speed from documents. AlgoDocs supports data extraction from multiple file formats such as PDF, scanned image, Hand written notes and other types of formats. You can easily   integrate AlgoDocs AI platform to various their party apps.

Try AlgoDocs Free-Forever Plan & Access Our Premium Features Without Any Additional Cost

• Docsumo : Docsumo is a document AI platform designed for data extraction from various types of documents. It supports a variety of document types, including bank statements and other documents.
• Parseur : Parseur is an AI-powered document processing tool that extracts data from PDFs, handwritten notes, and various other documents. It integrates seamlessly with other business applications for data extraction activities
• Nanonets : Nanonets offers comprehensive OCR solutions for data extraction from different types of documents.
• Docparser: Docparser is a handy document processing tool that extracts data from PDFs, scanned documents, and emails

How AlgoDocs Can Extract Data from a Bank Statement

AlgoDocs is an AI-powered Intelligent Document Processing tool that combines OCR and AI technologies. It makes bank statement extraction easy and reliable. Here’s how it works:

Step-by-Step: How to extract Bank statement data Using AlgoDocs?

Step 1: Login to your Algodocs account and go to the home page which is the Dashboard.

Step 2: Click on the Extractor tab , and you will notice on the right side of the Extractor tab, populated option to choose what kind of extractor you want to create.

Step 3: Click on Custom, and it will pop up a new window to name the extractor.

Step 4: Upload the sample PDF file, then click on Create Extractor. The Window will close, and you will be able to see your extractor on the folder as below,

Step 5: Click on the Manage tab , and you will be taken to the field/table creation page.

Step 6: Click on the +Add  , and it will show the extraction methods options.

Note: We used a bank statement containing two different accounts, so in the page selector in the extractor editor we applied some settings.

Step 6a: Click the page selector drop down menu, select

range of pages based on contentsselect

the Define Range options , and input the value

Step 7: Click on Form Data Extraction, this will launch a new window preview the sample PDF

document you uploaded. Click on Continue , this will open a new window with all the detected table and its values AlgoDocs AI.

Step 8: Use the Keep Rows Filter to keep Account Number.

Step 9: Use the Alter Columns Filter, select Remove Specific Column Filter to remove column 1

Step 10: Then convert the value in the remaining column to text, by selecting the Convert to Text filter.

Step 11:   Now we add a new field using Field/ text to Table Extraction Method to capture Account Name.

Step 12:   Drag your cursor over account name to select sample area for data capture.

Step 13: Use the Crop Text Filter and Specify End Position to capture the first line of the data

Step 14: Now add a new Field and Select Table Extraction under Rule-Based Data Extraction

Step 15: Align the columns separators accordingly and use the add column button to add as many separators as you want. Then click continue.

Step 16: Use the keep section filter to eliminate data that is not part of the table contents.

Step 17: WeUsed the Condition option, start section where column 2 contains Description and end section where column 2 contains End of Transactions, then we check the “Exclude this row” and “Find all Sections” checkboxes.

Step 18: Next, we keep rows where column 1 contains a value, as this takes out empty rows.

Step 19: Now we set Column Headers.

Step 20: With this done, we can save and exit the extractor editor

Step 21: Now we head to the extracted data section and select the extractor from the extractor lists. We have the option of single export to EXCEL, JSON, CSV and XML or bulk export as a combined Excel file.

Step 22: For this example, we selected bulk export to Excel and here we can see the results.

Conclusion

Extracting data from bank statements is essential for many industries. Manual extraction has many challenges, but OCR technology offers a solution. Tools like AlgoDocs make bank statement extraction faster and more accurate, fitting seamlessly into existing workflows.

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