OCR in Banking: The Guide to Enhancing Banking Operations with OCR Technology

OCR technology in banking automates converting bank statement images into editable and searchable text, which is further used for analysis, reconciliation, and financial reporting. Learn the key benefits and applications of OCR in banking.

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OCR has streamlined data extraction and processing across industries, from healthcare and education to real estate, lending, insurance, and lending. Banking is no exception. It improves data extraction accuracy, efficiency, and convenience for banks, helping them improve customer service.  

In this blog, we’ll examine how OCR technology in banking changes how things work and explore multiple use cases, benefits, and tools for using it efficiently. 

Understanding OCR in Banking

OCR technology in banking automates converting bank statement images into editable and searchable text, which is further used for analysis, reconciliation, and financial reporting. 

Optical Character Recognition (OCR) converts images of any text into machine-readable text. It's often used to recognize text in scanned documents and images.

OCR systems are widely used in KYC processes and to create PDFs from hard copies. They extract and validate documents and IDs with AI and intelligent document processing (IDP) systems to authenticate fields like names, addresses, social security numbers, etc. 

With OCR, banks can process loan and account opening/closing applications much faster. Additionally, mobile banking apps often use OCR to allow users to deposit checks by simply taking pictures with their phones.

Since documents become searchable, traceable, and easily accessible, OCR in banking creates a well-oiled systemization of improved data management and fraud detection. 

Benefits of OCR for Banking

Any technology that can automate a traditional industry (dependent on numerous manual tasks and heavy data extraction) is bound to have many benefits. Let’s dive into the benefits of OCR technology in banking.

Benefits of OCR for Banking
  • Increased operational efficiency: Rather than manually typing everything in, OCR can instantly scan a document and turn it into editable text
  • Improved accuracy:  Since manual data entry is prone to typos and errors, OCR can flag mistakes that may have slipped manual validation
  • Reduced costs: A dedicated OCR software, over time, will require fewer full-time employees (FTEs) as it can process documents at scale without manual intervention 
  • Enhanced customer experience: The OCR platform processes bank customer applications faster, reducing paperwork delays  
  • Improved data management: Organize all the documents efficiently, making them easily searchable and digitally accessible everywhere
  • Better decision-making: With all data accessible within seconds, you can make timely, data-driven decisions based on the extracted information 
  • Faster customer onboarding: OCR-based IDV checks allow banks to swiftly screen the customer identities before onboarding them or processing payments 

Common OCR Use Cases in Banking

Let’s delve into some OCR use cases in banking to see how the banking industry can explore the full capabilities of OCR:

Common OCR Use Cases in Banking

1. Loan processing

An OCR software scans loan applications, mortgage loan processing forms, tax forms, and income proofs to automatically extract information, including applicant details, income figures, and employment history. 

Data extracted can be pre-filled into loan application systems, speeding up the process and reducing manual work. Moreover, pre-populated forms with extracted data can offer loan officers a clearer picture of the borrower's financial health, making data-driven decisions easier.

OCR technology can handle handwritten loan applications which was earlier a challenge for traditional methods. This will open up opportunities for quick approvals, especially for micro-loans or rural banking initiatives.

For example, National Debt Relief, a New York-based BBB A+ accredited consolidation debt company, wanted a faster solution to reading debt settlement letters. The challenge was that they had no fixed structure, and with a team of 50 people, scanning 35,000 letters seemed impossible.

They used Docsumo’s pre-trained APIs with OCR and AI to ingest debt settlement letters easily. Docsumo’s rule-based validation engine applied contextual data validation on extracted data, which was later integrated with Salesforce. 

This resulted in 70% reduced operational cost, 99% accuracy due to automation, and 95% touchless processing.

2. Digitized customer onboarding

The major challenge with traditional customer onboarding in banks is the lack of coordination between various departments responsible for data collection, information authentication, processing customer applications, and moving along the application. 

Data needs to be more organized in various incompatible formats and outdated legacy systems. Due to the above-mentioned challenges, prospective customers are not uncommon to abandon an onboarding process midway. 

Banks are losing their customers and revenue to new-age fin-tech companies with faster onboarding with advanced technologies. 

OCR systems in banking use AI and ML algorithms to collect and sort data, verify e-signatures, and offer insights to simplify the customer onboarding process. The OCR technology can scan and extract data from bank statements, identification documents, and application forms and process the verified information within 45-60 seconds compared to manual data processing, which takes weeks. 

Customers can upload their documents to the online portal, and OCR technology with AI and ML can flag inconsistencies without manual intervention. 

3. Regulatory compliance

Banks need to maintain detailed records for audits and anti-money laundering checks. OCR technology can scan compliance documents like customer transaction records and risk assessments and organize and categorize this data for quick verification.  

OCR platforms analyze historical transaction patterns, identifying suspicious behaviors early on. This is possible once OCR is used to develop real-time monitoring systems to identify potential compliance risks basis high-alert keywords such as “high-risk countries” or “unverified sources.” 

4. Document digitization

OCR helps create digital records of decade-old paperwork for faster retrieval and verification. This unlocks the potential for OCR software in banking to analyze trends, identify patterns, and improve customer service based on information that is now readily available. 

For example, if banks want to campaign to promote their new home loan, they can research using OCR to analyze past loan applications to identify patterns of successful borrowers. With the findings of this research, they can target the product to the demographics and psychographics of the successful borrower’s target group. 

5. KYC (Know Your Customer) verification

Say a customer uses a banking app, applies to open a bank account from a remote location, and submits utility bills as address proof. An OCR platform can extract and verify address details against the national address database.

It can handle various documents, from KYC documents to passports, driving licenses, and utility bills, making it critical in developing countries where alternative forms of identification are common. 

OCR technology extracts information from financial documents and archives it. It can also help detect fraud and flag inconsistencies in documents. 

Implementing OCR in Banking Operations

OCR has revolutionized document-based processing in the banking and financial sectors. This can be observed in the lack of dust-filled archival rooms, faster approvals, quick onboarding, and enhanced client satisfaction. 

Implementing OCR in Banking Operations

1. Assessing business needs

What are the bank's challenges? They could be as simple as long wait times in application processing or delayed customer onboarding. 

Understand how data flows through departments across loan processing, KYC verification, account opening, and check processing to identify areas with a high volume of paperwork and manual data extraction. These processes are ripe for OCR-based digitization. 

2. Establishing a clear implementation roadmap

Consider factors like data processing volumes, existing legacy systems, staff preparedness, and processes that need to be digitized, along with budgets and resources needed for a successful rollout. 

Consider deploying OCR in a specific department followed by a phased rollout across the bank. For example, it could be the lending department, where document verification takes weeks and has a high applicant drop-off rate. 

3. Choosing the right OCR solution

Consider the following factors: 

  • Accuracy rates (OCR solutions with high accuracy rates for documents processed like tax forms, loan documents, and KYC), 
  • Document handling capabilities (if it handles structured formats like PDF, JPEG, TIFF, unstructured formats like handwritten forms or notes, or semi-structured documents), 
  • Scalability (if it grows with your expanding needs like high volume document processing) and, 
  • Integration capabilities (pre-built APIs or compatibility with tools you use already)

Request for product demos and case studies, and consider the industries they cater to get the right picture.

4. Integration with existing systems

Collaborate with the IT department and understand what OCR software’s technical team needs for a smooth implementation to ensure data flows efficiently, minimizing manual work. This can involve configuring APIs or pre-built connectors between the OCR platform and core banking systems. 

The OCR software you choose must integrate with the existing tech stack, including the CRM, ERP, and other systems. 

5. Training and support for users

Implementing a successful OCR solution relies on your staff’s adoption and proficiency.  Explain to the staff how technology will not replace them but improve their productivity to remove the adoption resistance. 

Train them to use the OCR system to maximize its adoption. 

Bring in the OCR solution’s support staff to conduct in-depth training of their software and collaborate with the IT team to propagate the learnings better within the staff. This is to keep checking on software updates and new features you want to apply. 

6. Continuous monitoring and optimization

Continuously monitor the performance of OCR solutions to optimize it and achieve your goals on time. Regularly assess accuracy rates, error rates, and processing times. 

Actively take feedback from your bank’s customers regarding their experience with the OCR system. Implement relevant suggestions to enhance consumer experience and peak performance. This might involve fine-tuning OCR settings with the team, redefining workflows, or exploring advanced features offered by the OCR solution.

7. Ensuring data security and privacy

Implement strong security measures, such as double encryption, role-based access, encryption at rest or in transit, and double security verification, like OTPs or fingerprints, to garner user trust.  

The OCR platform must comply with relevant data privacy regulations in your industry and country. 

8. Customizing workflows

OCR in banking requires tailoring workflows for specific documents. 

For example, loan applications with scanned income documents will be sent automatically to the loan processing department for further analysis. Pre-populate relevant fields in existing forms based on the extracted data from income tax forms, bank statements, or any other to eliminate manual data entry steps.

Transforming Banking With OCR Sytems 

The benefits of implementing an OCR platform in banking cannot be overlooked, especially when new-age fintech systems are competing with banks for customers and revenue. 

They include: 

  • High customer onboarding rate,
  • Higher staff productivity 
  • Improved data extraction accuracy 
  • Lower operational costs 
  • Digitization of documents leads to quicker searchability and improved accessibility 

For consumers, it means lesser anxiety when uploading multiple documents, faster approvals, less to and fro, and quicker loan processing. It seems like a win-win, but now everything depends on a competitive OCR software that’ll help you achieve all of this and some more. 

Here comes Docsumo’s OCR platform for banking, which has 30+ readymade APIs to get you a headstart, extract data from multiple document formats from structured, unstructured to semi-structured, elevate 50-70% efficiency with increased speed and accuracy, and enable touchless processing with over 99% accuracy.

Get in touch with Docsumo for OCR, talk to our experts, and start your free trial today.

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Written by
Karishma Bhatnagar

Karishma is a passionate blogger who comes with a deep understanding of Content Marketing & SEO tactics. When she isn’t working, you’ll find her in the mountains, experiencing the fresh breeze & chirping sounds of birds.

What are the main challenges of implementing OCR for banking?

Some main challenges banks may face while implementing OCR technology are document variability, handwritten/faded/blurry text, sensitive information handling, privacy and security concerns, resistance to change, and cost considerations.

What should banks consider when selecting an OCR solution?

When considering OCR solutions or software, banks must consider the accuracy rate, multiple data extraction/document processing capabilities, extraction complexity, security features, integration capabilities, scalability, additional support or training support, deployment options, and demo or free trial options.

How does OCR improve regulatory compliance in banking?

OCR improves regulatory compliance in banking by automating data extraction from multiple reports, reducing human errors, and increasing efficiency. Also, easier document retrieval during fraud checks or auditing helps maintain compliance.

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