Introduction
With the digitization of the world, the banking sector saw a tremendous shift from traditional to technologically driven banking practices.
Optical Character Recognition, or OCR in banking, has significantly transformed the banking sector in recent years. The technology is a revolution in the banking industry that has considerably improved a bank’s document handling.
Industry reports from 2023 reveal a significant shift in the BFSI (Banking, Financial Services and Insurance) vertical, with the banking sector accounting for a substantial 45% of the market share in the optical character recognition market segment.
Moreover, with the evolution of OCR systems and OCR automation, the banking sector has seen greater heights of efficiency.Through the technology, banks have streamlined their document processing with accurate results while significantly reducing the time, cost, and effort put in by their various resources.
This blog will focus on OCR in the banking industry, the various use cases of the technology in banking and its future trends.
OCR In Banking
OCR in banking has transformed a range of manual processes into digitalized processes improving document digitization through intelligent document processing.
This transformation has led to the digitization of various manual processes of the bank. As a result, the bank’s human resources have been freed from doing monotonous data entry tasks.
Further, the bank’s human resources are now able to focus more towards strategic and high-value tasks, leading to professional growth and development.
With enhancements in traditional OCR owing to Generative AI, which has improved AI text recognition, the banking industry is on the way to reaching greater heights in the near future.
What Is OCR?
OCR, or Optical Character Recognition, is a technology that initially helped with only text recognition but now also helps to convert text from various other formats, like images.
With the help of OCR in banking, banks can convert a range of documents like PDF files, scanned papers, or even images into searchable and editable formats and data.
Optical Character Recognition has led to improvements in every industry, specifically the banking industry where the volume of data has always been very large.
With this technology, banks have been able to automate their data processing tasks, reduce human resource manual efforts, and ensure accuracy in their document processing.
OCR In Banking – How It Works?
OCR is essentially a text recognition model that helps in text extraction from images. It also helps to scan text from images and recognize text from images.
In the banking sector, Optical Character Recognition is employed in various functions to help streamline processes and improve banks’ operational efficiency.
From customer forms, identification documents, financial statements, and even transaction records, OCR in banking has seamlessly integrated into many operations.
Below is a series of steps employed to complete the process of OCR text extraction in the banking industry –
- Document Capture—The OCR system starts by capturing documents requiring text recognition using various devices, such as scanning devices, mobile cameras, or other document processing systems. In the banking industry, such documents include bank statements, customer IDs, loan applications, or any other paperwork relevant to banking operations.
- Image Processing—In the next step, the scanned text from images undergoes image processing to improve clarity and remove imperfections or distortions. As a result, the Optical Character Recognition algorithms can interpret the text extracted from the image more accurately.
- Text Recognition—This step helps the technology recognize text from images after OCR text extraction. Using advanced pattern recognition techniques, the technology can analyze document images pixel by pixel.
- Character Segmentation—In cases where the conversion of handwriting to text is required, Optical Character Recognition systems use character segmentation algorithms to isolate every individual character and symbol. This process is important for accurate text recognition, as handwriting differs in style and legibility.
- Language Analysis—OCR systems use language analysis to enhance the OCR to text recognition accuracy levels in multilingual documents. The algorithms of the technology employ language-specific dictionaries and grammar rules to improve their interpretation and text recognition abilities.
- Data Extraction—Once the text is recognized using intelligent document processing, text extraction is carried out to extract relevant data. In the banking industry, the relevant data includes account numbers, transaction amounts, dates, names, addresses, and other key identifiers.
- Verification and Validation—OCR systems often use various validation checks and error correction mechanisms to ensure the integrity and accuracy of the extracted data.
- Integration with Banking Systems—Once the process of document processing is completed, the extracted data is seamlessly integrated with the banking systems and workflows for further processing and analysis. As a result, banks can improve their efficiency owing to the reduction of manual data entry and enhancement in automated decision-making processes.
OCR Use Cases In Banking
Where earlier banking documents were processed in 5-7 days, nowadays they are being processed in a matter of minutes! All this has been possible due to the optical character reader used in Optical Character Recognition systems.
OCR in banking has a widespread application spread across many functions focusing on intelligent document processing.
According to a report by Gartner, it has been estimated that the intelligent document processing (IDP) market size will reach $2.09 billion by 2026, with a CAGR of around 13% from 2021 through 2026.
Out of the entire IDP market, the banking sector forms a great part of the market with many use cases of OCR in banking.
Data Extraction From Bank Statements
With the introduction of OCR text extraction in the banking industry, banks have seen an exponential increase in efficiency and productivity owing to AI text recognition.
Optical Character Recognition in banking helps banks to extract essential information from the bank statements like account numbers, transaction details, and balances helping in document digitization.
With such automation, the banking reconciliation processes are streamlined while improving operational efficiency.
- Advantages—Data extraction from bank statements reduces manual effort, accelerates processing time, and enhances accuracy.
- Disadvantages—Due to the use of traditional OCR, errors in data extraction can be seen where poor-quality images or handwritten text are present.
- Solution—This limitation has been resolved with the evolution of Generative AI in OCR, which has led to accurate data extraction from even handwritten text and poor-quality images.
Loan Application Processing
Another area where OCR in banking has led to automation is the field of loan application processing – one of the most significant features of any bank.
The technology helps in faster data extraction of applicant information from loan applications because of AI text recognition and optical character reader.
As a result of the technology, banks can provide faster approval processes and improved customer satisfaction.
- Advantages—Optical Character Recognition automated loan application processing leads to seamless integration with loan management, accelerated processing time, and enhanced accuracy.
- Disadvantages—Traditional OCR technology requires manual intervention to handle non-standard formats or documents with varied layouts.
- Solution—This limitation has been resolved with the introduction of Generative AI in Optical Character Recognition, which has led to accurate data extraction without manual intervention for non-standardized formats and documents with complex layouts.
Compliance and Regulatory Reporting
Intelligent document processing helps banks automate compliance tasks by data extraction from regulatory documents.
With text extraction, banks can comply with all their legal requirements without facing any consequences for non-compliance.
- Advantages—OCR in banking allows banks to improve their regulatory compliance, reduce their risk of errors, and enhance their audit trail transparency.
- Disadvantages—The accuracy of the text extraction may be affected by the interpretation of complex regulatory language or changes in regulatory standards.
- Solution—This disadvantage has been resolved with Generative AI in Optical Character Recognition, which, owing to its iterative and continuous learning through feedback feature, remains current with all the regulatory and compliance landscape changes.
Customer Service and Support
With the evolution of the technology, customers have seen a tremendous improvement in their banking experience.
As a result of the text recognition model, banks efficiently retrieve customer information through Optical Character Recognition document scanning.
- Advantages—Introducing the technology in customer service and support in banking reduces response times, enhances customer experience, and improves operational efficiency.
- Disadvantages—Due to the use of traditional OCR, errors in data extraction can be seen where poor-quality images or handwritten text are present, leading to processing delays.
- Solution—This limitation has been resolved with the evolution of Generative AI in Optical Character Recognition, which has led to accurate data extraction from even handwritten text and poor-quality images, resolving processing delays in banking.
Fraud Detection and Risk Management
OCR in banking, with the help of AI text recognition, helps the banking industry by identifying potential fraudulent activities.
The identification of fraudulent activities is done with the help of intelligent document processing, which analyzes transactional documents and detects exceptions.
- Advantages—OCR systems help banks with improved security measures, early detection of fraudulent activities, and enhanced risk assessment.
- Disadvantages—Traditional Optical Character Recognition technology may lead to reduced accuracy where sophisticated fraud schemes are applied or irregular document formats are seen.
- Solution—This limitation has been resolved by introducing Generative AI in Optical Character Recognition, leading to accurate data extraction for non-standardized and irregular formats and documents with complex layouts, improving fraud detection and risk management.
OCR In Banks: Future Trends
With the ever-evolving digital landscape, OCR in banking will also evolve, leading to greater advancements.
Keeping the evolution of Generative AI in Optical Character Recognition, the following future trends can be seen in the field of banking –
- Complete digitization of all the documents required in the banking industry, whether ForEx-related documents, loan processing documents, or even documents of mergers and acquisitions with the help of Multi-Modal data capture OCR solutions.
- Enhanced anticipation of customer needs, detection of frauds, and optimization of processes leading to greater profitability and efficiency owing to the usage of AI text recognition and OCR systems leading to predictive analysis.
- OCR in banking will also see an increase in Voice Recognition and Audio Transcription, leading to the generation of additional jobs for physically challenged people, and record customer interactions. This has various benefits with respect to economic boom, compliance, and customer satisfaction.
Conclusion
The integration of Optical Character Recognition systems in the banking industry has led to great improvements and developments in the industry.
With improvements in efficiency, professional development and focus on high-value tasks, OCR in banking has proved successful.
We at CrossML truly believe that the banking industry is the backbone of any country’s growth!
Therefore, we at CrossML, with our dedicated team members, develop newer and enhanced Optical Character Recognition solutions for the banking industry, further minimizing manual efforts and time, and improving efficiency, and profitability.
FAQs
OCR improves banking efficiency by eliminating manual data entry tasks and minimizing errors through text extraction and recognition. Additionally, due to eliminating manual data entry tasks, banking staff focuses on high-value tasks that lead to better efficiency.
OCR is important for data processing in banking as it accelerates tasks like analyzing bank statements, loan processing, fraud detection, etc. By quickly digitizing documents and analyzing them, the technology helps to improve accuracy and reduce bank processing times.
Banks' challenges when implementing OCR include overcoming employees' resistance to change and integrating the OCR system with existing bank workflows.
The future of OCR in the banking sector is extremely bright. Advances in Artificial Intelligence will help in the transition of banking processes to being fully digitized.
