Bank statement extraction process involves advanced technologies like OCR and AI to convert PDF bank statements into usable data. Read the blog to learn the process of data extraction from bank statements, its challenges and ways to automate it.
Capturing data from PDF bank statements is a critical task for organizations seeking valuable financial insights. Finance managers rely on this data to make informed decisions, perform bank statement analysis, and create accurate budgets. Loan officers use it to verify applicants' income and expenses, ensuring proper assessments for loans.
Contemporary technologies such as optical character recognition (OCR), intelligent document processing (IDP), and rule-based systems enable extracting data from PDF bank statements in an efficient and accurate manner. Banking, lending, and financial services further leverage the wealth of information within bank statements.
In this article, we cover elements of bank statements and how to capture crucial data points from these documents. So, let’s jump right into it:-
What are bank statements?
Bank statements offer an overview of the customer’s financial transactions, and their online versions often come in PDF format and are secured by a passcode.
Finance managers skim through these statements to gain insights into spending patterns, identify potential cost savings areas, analyze cash flows, and monitor account balances.
Underwriters use it to assess an applicant's financial health, verify income and expense levels, and evaluate creditworthiness.
Banking and finance organizations use bank statements to identify spending patterns, improve tax reports, validate large transactions, conduct reconciliations, and highlight cash outflows.
Preparing PDF bank statements for data extraction
Despite the inherent complexities associated with financial data extraction from PDF bank statements, there are effective strategies to overcome common obstacles. Here’s how data is extracted from bank statements.
Clean up the PDF Files
Intelligent document processing software uses advanced image recognition software to deskew the images, reduce noise, and convert the file into grayscale to prevent colors from interfering with the data extraction.
Deskewing
Deskewing straightens the documents using AI to remove any inclines and awkward angles. It makes the documents more readable.
Denoising
Denoising involves removing unnecessary marks, printing spots, and uneven contrasts from PDF documents.
Grayscale conversion
Also known as binarization, converts PDF documents into a grayscale format to prevent colors from impeding the data capture process.
Make the PDF machine-readable and searchable
Most automated data capture tools come with either rule-based or ML-based solutions.
Rule-based extraction
In rule-based extraction, the software first uses OCR (optical character recognition) technology to convert these images into machine-readable and searchable texts. OCR is typically used to extract field specific information from fixed template documents like PDF documents and images to accelerate the rate of approval for loans and new account applications.
AI/ML-based data extraction
Similar to rule-based extraction, AI/ML-based data extraction uses OCR to convert PDF formats into machine-readable formats. The automated data extraction software uses MultiModal learning and artificial intelligence to extract valuable data from these bank statements.
Common issues associated with data extraction from PDF bank statements
The most common issues that plague the data capture process for PDF bank statements are:
Password protection
PDF bank statements are often encrypted with passwords to ensure data security. However, this can hinder data extraction efforts. Prior to extraction, it is essential to have the necessary credentials to unlock password-protected PDFs or obtain unencrypted versions for seamless data extraction.
PDF table extraction
In addition to images, text, and figures, PDF bank statements contain tables, wherein lies important information. A PDF converter processes the entire document without providing an option to limit the data extraction to specific sections in the PDF such as specific columns and rows.
Data extraction process with Docsumo: Configuring bank statement processing
Docsumo’s advanced AI/ML algorithm and OCR technology help financial institutions effortlessly convert bank statements into actionable findings. Here’s a breakdown of how easy it is to extract data from bank statements using this intelligent platform:-
Step 1- Uploading PDF bank statements to the Docsumo platform
Upload the unencrypted PDF bank statement to the Docsumo platform. The pre-trained APIs identify key information, like account numbers, transaction IDs, summary tables, and transaction amounts.
Step 2 - Initiating the extraction process
Docsumo's advanced data capture algorithms, powered by AI and OCR, start the extraction process. The key information is intelligently extracted from the statements.
Step 3 - Data validation & reviewing extracted data
The extracted data from the bank statements is sent to the relevant department for thorough review and approval. Docsumo's API ensures 99% data accuracy throughout the process. It also highlights mismatched entries, allowing the authorities to validate the information with ease.
Step 4 - Handling exceptions and improving future extraction results
Any exceptions or unforeseen errors are immediately flagged, and the platform automatically notifies the respective personnel for the manual verification of the extracted data. The ML algorithm records these adjustments and uses them to refine its future processes.
AI-Enabled Automation for Bank Data Extraction
Achieve 99% accuracy with Docsumo’s automated bank statement extraction.
Integrating Docsumo with existing systems and workflows
Integrating Docsumo with existing systems and workflows streamlines data transfer, saving time and improving accuracy. Third-party integrations allow seamless data transfer to downstream apps, preventing errors and reducing manual consolidation efforts.
For accounting teams, Docsumo integrates with Stripe, QuickBooks, Google Sheets, and Xero, serving as a single source of truth. Native integration capabilities with cloud storage systems, like OneDrive, simplify data aggregation, enabling large imports and reducing dependence on heavy IT infrastructure.
Docsumo integrations with Zapier help businesses automate operations, enhance efficiency, and leverage advanced document processing for data-driven decisions.
Data security and compliance considerations
Ensuring data security and compliance with industry regulations is paramount when it comes to data collected and stored from bank statements. Financial institutions, insurance companies, and mortgage lenders need to adhere to industry regulations, like GDPR and SOC-2.
These security certifications build customer trust by maintaining the confidentiality of collected user data. Furthermore, these government regulations implement stringent standards for the storage, handling, and processing of such sensitive information.
Ensure that the intelligent data capture software you implement for document processing is SOC-2 compliant and GDPR certified.
SOC-2
Docsumo got the SOC-2 certification in September 2021; which implies that the platform protects customer data and safeguards their privacy. In addition, the certification ensures that the software has the necessary audit controls in place along with reliable measures to tackle any cyber threats.
GDPR
All the data processed by Docsumo is in accordance with GDPR’s terms. These terms make Docsumo the data processor for imported documents and parsed content, and it acts as the data controller for the personal data collected from these bank statements.
Case Study: Hitachi Streamlines Bank Statement Reconciliation using Docsumo
Hitachi, a white-label ATM provider, was overburdened with the volume of monthly bank statements sent to them by their ATM operators. It had become challenging for them to manually process over 3000+ bank statements every month. This is where Docsumo intervened and streamlined its processes. So, what were the challenges, and how did Docsumo alleviate their reconciliation concerns?
Challenges:-
- Manually scanning bank statements with 50+ different formats and structures.
- A dedicated team of underwriters and data entry operators extracted information from 3000+ bank statements every month.
- Absence of data validation processes.
- Double manual entry was mandated for all documents.
Solution introduced by Docsumo:-
- Automatic data capture using pre-trained, AI-based APIs with 99% accuracy.
- The team only had to review exceptions.
- ML-based smart data extraction API could process more than 50+ bank statement formats and structures with ease.
- Docsumo’s custom rule-based approach auto-classifies letters and validates bank statements in real time.
- 95% STP helped Hitachi reduce their bank statement processing time to less than 30 minutes.
Conclusion
Docsumo streamlines data extraction from bank statements and simplifies the workflows for financial institutions, mortgage lenders, and insurance companies. What sets Docsumo apart from the rest of bank statements data capture platforms is-
- 99% data extraction accuracy
- 95% STP rate increases the overall efficiency of your workflows
- Docsumo reduces document processing times from hours to minutes
- Automatic validation and verification of the extracted data
If you’re looking for a reliable platform to distill important information from bank statements, sign up for a 14-day free trial.
