01. The Challenge
A large financial services company, Lazard, faced significant challenges in its document verification process. The traditional manual verification method was time-consuming, error-prone, and required substantial human resources. This inefficiency led to delays in customer onboarding and loan processing, ultimately affecting customer satisfaction and operational costs. With increasing competition, FinancePlus needed a more efficient and reliable solution to maintain its market position.
Key challenges included:
- High Volume of Documents: Processing thousands of documents daily, including IDs, bank statements, and contracts.
- Human Error: Manual verification was susceptible to errors, leading to potential fraud risks and compliance issues.
- Operational Bottlenecks: Long processing times delayed customer onboarding and service delivery.
- Compliance and Security: Ensuring that the document verification process met strict regulatory requirements and maintained high security standards.
02. The Solution
Lazard partnered with our AI agency to implement an Automated Document Verification system using Computer Vision technology, leveraging the Document Text Recognition (DocTR) framework. Our solution included the following components:
- DocTR Integration:
- Implemented DocTR to extract and validate text from various document types, ensuring high accuracy and speed.
- Used pre-trained deep learning models for text recognition, fine-tuned to handle specific document formats and languages.
- Automated Workflow:
- Developed an end-to-end automated workflow for document submission, verification, and approval.
- Integrated with FinancePlus's existing customer management and compliance systems for seamless operation.
- Real-Time Processing:
- Enabled real-time document verification, reducing processing time from days to minutes.
- Provided immediate feedback to customers, improving their experience and reducing wait times.
- Fraud Detection:
- Implemented advanced algorithms to detect anomalies and potential fraud in submitted documents.
- Enhanced security features to ensure data integrity and compliance with regulatory standards.
"For document classification, we employed the YOLO model. For information extraction, we initially used Tesseract but later transitioned to DocTR. This shift was driven by DocTR's superior ability to accurately extract information from images of highly variable quality" - Nick Terens, Senior Data Scientist at Aidentico
03. The Result
The integration of Automated Document Verification with DocTR resulted in significant improvements for Lazard:
- Increased Efficiency:
- Reduced document processing time by 85%, enabling faster customer onboarding and loan approvals.
- Freed up human resources to focus on more complex tasks, improving overall productivity.
- Enhanced Accuracy:
- Achieved a 99% accuracy rate in document verification, significantly reducing errors and fraud risks.
- Improved compliance with regulatory requirements, ensuring high standards of data security and integrity.
- Cost Savings:
- Decreased operational costs by 60% through reduced manual labor and faster processing times.
- Lowered the risk of financial losses due to fraud and non-compliance penalties.
- Improved Customer Satisfaction:
- Enhanced customer experience with quicker turnaround times and reliable verification processes.
- Increased customer trust and loyalty, contributing to a 20% growth in new customer acquisitions.
- Scalability:
- The solution was scalable, allowing FinancePlus to handle growing document volumes without compromising on speed or accuracy.
- Positioned FinancePlus as a technology leader in the financial services industry, attracting more clients and business opportunities.
