The TEXTRAIR framework has a built-in smart library and proprietary algorithms for extracting data from pdf and digitized bank statements.


Its primary benefit is that it can process bank statements automatically, avoiding the difficulties associated with optical character recognition (OCR) and thereby maximizing the information present, converting it into actionable insights.

Bank transactions are categorized so that they correspond with the client's final or provisional accounting data submitted for loan approval. A straightforward interface allows the lending team to manually confirm data extraction in cases where the confidence level is low and to classify previously undefined elements. Unlike cloud-based alternatives, our window program stores data locally on the client's machine, making it the safest option for a bank to implement. Take advantage of an extensive collection of prebuilt bank statement templates configured from NNN banks worldwide to precisely capture header and transaction data with possibilities to support various variations at the push of a button.

Case Study


The digitalization of Bank Statements

  • Streamline Bank Statements management
  • Strengthen the usefulness of data included in bank statements.
  • Use the Visualization Dashboard to gain insightful information from your bank statement.
    • Intelligent TEXTRAIR algorithms process bank statements in 500 milliseconds with 99% accuracy in real-time and convert 100 pages to Excel in less than one minute.

    Challenges


    Banking institutions interact with hundreds of formats, including variations in tabular formats, necessitating manual efforts and a significant investment of time from the operations team to extract data using classic OCR, which cannot extract correct and trustworthy data.

    The classification of bank entries, the derivation of important attributes like sales, vendor payment, salaries, rent, etc., the reconciliation of these periodic expenses and income, and the analysis of these characteristics for loan approvals all require a huge number of internal or outsourced workers, which banks employ in order to function. This method is not only tedious, but it also frequently produces incorrect results. As a result, the decision-making process slows down, negatively impacting customer service quality.

    Solution


    Step 1.
    Convert high-resolution (dpi >= 250) scanned bank statements to a digital format using our in-house developed technique for the task.

    Step 2.
    Data fields are extracted from tables by analyzing images, PDF text, and calculations. All attributes are retrieved from standard fields.

    Step 3
    Dockers with pre-installed dependencies can be deployed on-premises or in the cloud.

    Results


    The results of one of our client, who is managed services provider for a large bank, is as follows:

  • Possible annual cost savings of USD 135,000
  • It took less than 10 seconds to analyze 100 pages of bank statement data at a 95% confidence interval.
  • As a compiled report for a loan's final evaluation, released 15 Full-Time Equivalents (FTEs) from the client's managed operation of each bank process, for a total of 60 FTEs transferred to allied data services.

    • features;

    Rapid Procedure
    Converting 100 pages of a bank statement into Excel takes less than a minute, and the conversion is done in real-time using intelligent algorithms that require only 500 milliseconds.
    Bulk Uploads
    You can upload multiple files by dragging and dropping them onto the platform. When you utilize our API or cloud integrations, we can import your bank statement mechanically.
    Downloadable Data
    When you upload a PDF file to Textrair, you may easily transform it into a CSV, Excel, JSON, or XML file. Extractions can be made for any time period and saved in whatever format you desire.
    Higher-Level Image Processing
    The precision of data extraction is improved by using sophisticated image preprocessing (contrast correction, noise reduction, etc.).
    Optical Character Recognition Scanned Documents
    You can now easily convert scanned bank statements into text using our built-in, clever OCR engine.
    Protected Environment
    Since the data for a Windows app is stored locally rather than online, it is the safest option for a bank to implement
    Extract tables
    Bank statements are typically provided in PDF format. To facilitate further processing, extracting multi-page tables from these documents and exporting them as either Excel or JSON data is useful.
    Smart Filters
    You can transform raw data into a usable format using filters for dates, integers, and other regular expressions.
    Visualization Dashboard
    Graphical Categorical data visualization for in-depth analysis
    Bank Statement Variation Smart Libraries
    Over 400 sample bank statements with built-in smart libraries for extracting header and description text