What is OCR in Banking?

Key Highlights:

• Optical Character Recognition (OCR) technology eliminates the need for manual data entry by automating the data extraction from financial documents
• It automates document processing which reduces paperwork and manual intervention, leading to substantial cost savings.
• OCR accelerates customer onboarding by quickly scanning and verifying identity documents and e-signatures, reducing waiting times and enhancing the customer experience.
• Integrating AI and ML, OCR can be further improved to process complex, unstructured texts and documents while enhancing accuracy.
• Arya AI empowers banking and financial institutions to integrate AI-powered apps and solutions and successfully leverage OCR's applications in banking

Financial institutions handle large amounts of data from documents, driven by the need of risk management, compliance, insights, and operational efficiency. While handling these documents manually is tedious and next to impossible, Optical Character Recognition (OCR) technology simplifies and automates document processing and data extraction.

What is OCR (Optical Character Recognition)?

The OCR technology scans images and documents, such as bank statements, and converts them into editable, searchable, and machine-readable text for analysis, financial reporting, and reconciliation.

It tremendously eliminates tedious and error-prone manual processes of extracting data. In addition, this, in turn, improves data management and risks of data breaches and fraud.

Benefits of OCR in Banking

Poor data quality in banking, such as duplicate data and inaccurate figures, costs banking firms $15 million per year, according to a Gartner report. This significantly impacts the productivity and efficiency of banking operations—making adopting technologies like OCR critical to ensuring high data accuracy.

Here are the benefits of OCR in banking:

• OCR technology significantly reduces processing time by eliminating manual data entry processes for checks, invoices, or loan applications, improving operational efficiency and speed.
• OCR minimizes manual data entry errors by automating data extraction, ensuring consistent data capture and higher data accuracy in transactions and record-keeping.
• OCR reduces the need for manual intervention by automating data extraction in document processing, leading to less paperwork and substantial cost-savings.
• OCR enhances fraud detection by cross-checking documents and customer IDs and determining inconsistencies in customer details, financial records, and signatures.
• OCR streamlines storing and digitizing data and documents effectively, making retrieving data a breeze for compliance regulations. Moreover, OCR scans and verifies identity documents accurately, allowing banks to meet compliance regulations, such as KYC and facilitates customer onboarding.
• OCR improves data accessibility by converting physical documents into digital searchable formats for better analysis and decision-making.

How Does OCR in Banking Work?

OCR seamlessly integrates into several banking operations, such as KYC verification and customer onboarding, to help extract data from customer forms, financial statements, identity documents, etc.

Here’s how the OCR technology typically works in the banking sector:

• It first captures customer documents, such as customer IDs, loan applications, and bank statements, followed by document processing.
• In the next step, the OCR technology uses advanced pattern recognition to recognize text from images and analyze documents pixel by pixel. For a handwritten text conversion, OCR uses character segmentation algorithms to separate individual symbols and characters to ensure accurate text recognition.
• Next, to understand and interpret the context of the recovered text of an image, OCR uses language processing and analysis tools, which employ language-specific rules for better text interpretation. For example, distinguishing between two similar characters, such as '1' and 'I,' is essential and is possible due to language processing tools.
• Post text recognition, the next step is text or data extraction, such as extracting relevant banking details, like customer’s account numbers, addresses, names, dates, etc.
• Finally, the OCR technology then verifies and validates the extracted data to determine checks and errors and ensure data accuracy.

Once this entire process is completed, OCR seamlessly integrates the extracted data with the banking workflows and systems for further analysis. This significantly helps reduce manual intervention in data extraction, ensuring high data accuracy and efficiency.

Applications and Use Cases of OCR In Banking

Here are a few critical applications of OCR in banking:

1. Loan Application Processing

The OCR technology can scan and process loan applications, tax forms, mortgage loan applications, and customer income proofs for faster and more accurate data extraction of loan applicants' credit history, employment history, and income figures.

This results in faster data validation and loan approval—improving customer satisfaction and experience.

2. KYC Verification

OCR can speed up financial institutions' KYC verification processes by automating data extraction from several identification and KYC documents, such as passports, utility bills, and driving licenses – reducing manual checks and ensuring compliance with regulatory standards.

3. Customer Onboarding

It can scan and extract data from customers' identity documents, sort the extracted data, and verify e-signatures. The process takes a few seconds to complete, allowing financial institutions to onboard users more efficiently.

4. Regulatory Compliance

OCR can efficiently scan large volumes of customer data and transactions and ensure real-time monitoring. This can help identify and flag suspicious activities and transactions, allowing financial institutions to meet Anti-Money Laundering (AML) compliance requirements.

5. Fraud Detection and Prevention

OCR can help banks and financial institutions detect data discrepancies by cross-checking extracted data with internal or external databases.

This results in early fraud detection and enhanced risk assessment, preventing risks of compliance issues and reputational damages.

Steps To Implement OCR in Banking Operations

Here's a roadmap to guide banks to implement OCR in banking operations:

• Assess the banks' needs and challenges, such as delayed customer onboarding, long waiting times, or lack of efficient labor.
• Analyze factors such as legacy systems, staff responsibilities, data volumes, etc., and determine the department that needs OCR integration the most.
• Choose an ideal OCR solution that is highly compatible with banking needs and has high integration capabilities, document handling capabilities, scalability, and accuracy rates.
• Invest in reliable software and APIs that will ensure smooth OCR integration into existing systems with efficient data flow and minimal manual intervention.
• Monitor the OCR solution's performance regularly by assessing processing times, error rates, and accuracy rates and optimize the solution accordingly.

Future Trends of OCR In Banking

The global OCR market size, valued at $10.45 billion in 2023, is expected to reach $43.69 billion in 2032, growing at a 17.23% CAGR.

This growth is a testament to the surge toward growing digitization and automation across industries.

With the integration of AI and ML, OCR in banking is set to improve accuracy and data processing capabilities by more efficiently processing complex texts and documents.

How Arya.ai Can Help

To automate your banking operations, such as customer onboarding, KYC verification, etc., at Arya AI, we offer robust and secure apps, such as bank statement analyser, cheque extraction, KYC extraction, signature detection, and document fraud detection that uses sophisticated Artificial Intelligence and Machine Learning (ML) algorithms to extract accurate data from banking documents and validate against databases to determine anomalies, if any and ensure secure and accurate customer verification.

So, if you are looking to accelerate your banking operations, ensure higher accuracy rates, and minimize risks of fraud and identity theft, make sure to check out our extensive range of AI apps and choose the ones that best suit your needs and requirements.

Conclusion

Integrating Optical Character Recognition (OCR) in banking operations is a game-changer. It brings several benefits to the financial services industry, including improved efficiency, productivity, and accuracy by processing unstructured data in documents and images.

OCR in banking helps lead to faster and higher customer onboarding rates and lower operational costs, increasing the competitive rates for banks and financial institutions.