Automated Spreading in Banking: A Transformative Solution

The banking industry is rapidly evolving, and with it comes the need for new and innovative solutions to improve efficiency, accuracy, and productivity. One such solution is automated spreading, which uses optical character recognition (OCR), machine learning (ML), and Artificial Intelligence (AI) to automate the process of extracting data from financial documents and integrating it into financial analysis tools.

Challenges of Manual Data Entry

Manual data entry is a time-consuming and error-prone process that can significantly impact the efficiency and accuracy of financial analysis. Credit analysts often find themselves spending hours manually entering data from financial statements, tax returns, and other supporting documentation into spreadsheets. This process is not only tedious but also prone to human error.

In addition to the challenges associated with manual data entry, banks are also facing increasing pressure to reduce costs and improve efficiency. In order to meet these demands, banks need to find ways to automate their financial analysis processes.

How Automated Spreading Works

Automated spreading uses OCR, ML and AI together, to automate the process of extracting data from financial documents and integrating it into financial analysis tools. OCR is a technology that can recognize and convert text from images into digital format. ML / AI are fields of computer science that allows machines to learn without being explicitly programmed.

Automated spreading solutions typically work by following these steps:

  1. Upload financial documents: The automated spreading process begins with the uploading of financial documents. These documents can include a variety of financial statements, tax returns, loan applications, and other supporting documentation. The documents can be uploaded in various formats, such as PDF, scanned images, or even physical documents. The automated spreading solution should be able to handle a wide range of document formats to ensure compatibility with different types of financial records.
  2. Extract data: Once the financial documents have been uploaded, the automated spreading solution utilizes optical character recognition (OCR) technology to extract data from the documents. OCR is a sophisticated technology that can recognize and convert text from images or scanned documents into digital format. The OCR engine analyzes the document's layout, identifies text patterns, and extracts relevant financial information, such as account balances, transaction details, and financial ratios.
  3. Classify data: After the data has been extracted from the financial documents, the automated spreading solution employs machine learning (ML) algorithms to classify the extracted data into different categories. ML algorithms can analyze the extracted data, identify patterns, and categorize the information into relevant financial categories, such as revenue, expenses, assets, liabilities, and equity. This classification process ensures that the extracted data is organized and structured for further analysis.
  4. Integrate data: The final step in the automated spreading process involves integrating the classified data into a financial analysis tool. This tool can be a spreadsheet, database, or dedicated financial analysis software. The integration process seamlessly transfers the classified data into the analysis tool, allowing for further manipulation, visualization, and interpretation of the financial information. This integration enables credit analysts and financial professionals to perform in-depth analysis, generate reports, and make informed decisions based on the extracted and classified data.

Benefits of Automated Spreading

Automated spreading offers a number of benefits for banks, including:

  • Reduced time and costs: Automated spreading effectively eliminates the need for manual data entry, streamlining the process of extracting and integrating financial data. This automation can reduce the time spent on data entry by up to 75%, freeing up credit analysts to focus on more strategic and value-added tasks such as risk assessment and underwriting decisions. This shift in focus can lead to improved decision-making, enhanced customer service, and increased revenue opportunities.
  • Improved accuracy: Automated spreading eliminates the risk of human error by automating the data extraction and integration process. OCR and machine learning algorithms accurately capture and classify financial data, ensuring that the information used for analysis is precise and reliable. This improved accuracy leads to better decision-making, reduced risk exposure, and enhanced regulatory compliance.
  • Enhanced productivity: Automated spreading significantly enhances productivity by automating the data extraction and integration process. This automation allows credit analysts to handle larger volumes of data efficiently, enabling the bank to scale its operations and meet the increasing demands of the market. As a result, banks can process more loan applications, provide faster customer service, and expand their market reach.
  • Increased scalability: Automated spreading provides banks with increased scalability by automating the data extraction and integration process. This automation enables banks to handle large volumes of data efficiently and adapt to changing market conditions with ease. Banks can quickly process new loan applications, analyze emerging market trends, and identify potential risks, allowing them to make informed decisions and stay ahead of the competition.

Use Cases for Automated Spreading in Banking

Automated spreading can be used in a variety of ways in the banking industry, including:

  • Credit analysis: Automated spreading can be used to quickly and accurately extract financial data from loan applications and other supporting documentation. This data can then be used to perform comprehensive credit analysis and make informed lending decisions.
  • Underwriting: Automated spreading can be used to automate the underwriting process by streamlining the collection and analysis of financial data. This can help to reduce the time it takes to approve loans and get money into the hands of borrowers.
  • Risk management: Automated spreading can be used to identify and mitigate financial risks by providing real-time insights into customer portfolios and market trends. This can help banks to protect their bottom line and ensure long-term success.


Automated spreading is a transformative technology that can help banks to improve their efficiency, accuracy, productivity, and scalability. It is a key enabler of digital transformation in the banking industry.

There are several automated spreading solutions available on the market today and partnering with a Fintech makes good business sense. Vikar Technologies is one of the leading financial technology providers offering automated financial spreading as part of their One Vikar platform. Vikar is the only Fintech provider with flexible software solutions that enable financial institutions to digitally transform their entire loans and deposits processes without migrating off their current systems.  

For more information about this article please contact Amruta Dongre, CTO at or to learn more about Vikar products and services please contact Nancy Schneier at



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