Client Overview & Impact
Union Bank of India, one of the largest public sector banks in India, handles a wide range of retail and MSME lending requirements. The bank sought a solution to streamline and automate its extensive document processing needs, enhance data accuracy, and improve operational efficiency across retail and MSME lending journeys. Through a competitive RFP process, Union Bank selected Glib.ai (in partnership with Cygnet) to meet their IDP requirements.
Business Challenges
Union Bank faced several key challenges in its lending workflows:
High Document Volume
Processing an expected volume of 20 lakh documents annually across multiple document types (bank statements, financial statements, ITR, GST, and related documents) required a significant manual effort and workforce.
Manual Data Extraction and Validation
Traditional data extraction processes were time-consuming and often prone to human error, impacting the accuracy of risk assessments and compliance.
Turnaround Time (TAT)
Prolonged processing times created bottlenecks in both retail and MSME lending journeys, leading to delayed loan disbursements and impacting customer satisfaction.
Operational Scalability
With growing demand for retail and MSME loans, scaling document processing without proportional increases in manual resources was a major concern.
Glib.ai’s Solution: A Comprehensive IDP Implementation for End-to-End Automation
To address these challenges, Glib.ai deployed an Intelligent Document Processing (IDP) solution tailored to meet Union Bank’s specific needs. Key solution components included:
Automated Data Extraction Across Document Types
- Leveraging OCR and NLP, the IDP solution extracted data from various documents such as bank statements, financial statements, ITRs, and GST returns.
- Ensured data accuracy through real-time validation mechanisms, eliminating manual data entry and reducing errors.
Full Integration for Straight-Through Processing (STP)
- Enabled end-to-end straight-through processing (STP) of lending workflows, removing manual intervention and reducing TAT by 70%.
- Seamless integration with Union Bank’s core lending systems ensured that extracted data was directly available for decision-making, enhancing the speed and efficiency of loan processing.
Human-in-the-Loop (HILT) Interface
- Implemented a HILT mechanism for exception handling, allowing the team to review flagged documents that required manual intervention, while ensuring continuous system learning and adaptability.
Scalability for High Volume Processing
- The solution was designed to scale according to Union Bank’s requirements, handling up to 20 lakh documents per year without additional strain on operational resources.
Results and Expected Impact
Union Bank’s IDP implementation is currently in the User Acceptance Testing (UAT) phase, with a full go-live scheduled for November. The anticipated impact includes:
70% Improvement in Efficiency
Automation across retail and MSME lending workflows is expected to cut processing time by up to 70%, reducing bottlenecks and enhancing customer satisfaction.
High Data Accuracy and Compliance
Improved data accuracy through automated extraction and validation ensures compliance with regulatory standards and minimizes risk.
Operational Scalability
The scalable design allows Union Bank to accommodate increasing document volumes without expanding the manual workforce, promoting sustainable growth.
Conclusion
The IDP solution provided by Glib.ai has positioned Union Bank of India to improve efficiency, reduce costs, and offer faster loan processing times. This transformative project exemplifies Glib.ai’s commitment to delivering cutting-edge, scalable solutions that meet the evolving needs of the BFSI sector.
