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Our client is a leading North American-based commercial finance firm specializing in business credit and asset-backed loans. Serving the middle-market sector, they manage hundreds of loan applications each month and provide financial solutions across various industries.
Despite being a prominent player, the client struggled with legacy systems and manual processes for underwriting, which resulted in:
The client needed a workflow automation and intelligent document processing solution that could:
We developed a robust, cloud-native solution hosted on AWS, designed to automate the client’s document processing and underwriting workflows. The platform was built using a microservices architecture to isolate tasks, ensuring flexibility and scalability. It incorporated Amazon RDS as the primary database for structured data and a custom-trained IDP module powered by TensorFlow for data extraction and classification.
Solution Workflow
We conducted a comprehensive audit of the client's existing document archives, data infrastructure, and legacy systems. Our goal was to understand their data readiness, integration points, and governance needs. We also evaluated security requirements to ensure compliance with privacy regulations such as CCPA and SOC 2.
To digitize the existing unstructured documents, we used OpenCV-based OCR tools to scan and extract text from a variety of formats (PDFs, scanned images). We applied data engineering algorithms to standardize formats (dates, currencies, names) across all archived documents.
We built a proprietary IDP module to automate data ingestion, classification, and processing. Deployed on AWS Lambda, the module was connected to the client’s systems via secure APIs managed through Amazon API Gateway for seamless integration.
The module used custom-trained TensorFlow models to classify documents and extract critical data like:
We developed a dynamic workflow automation engine that computes a preliminary financial risk score from extracted data. Files that meet the pre-set low-risk thresholds were automatically fast-tracked, and automated notifications were sent to analysts for further action.
We implemented an MLOps pipeline on Amazon SageMaker to enable continuous training and model optimization. The solution maintained a 99.5% accuracy rate in data extraction.
30% reduction in operational IT costs.
40% higher underwriter throughput
50% reduction in manual effort needed for audit trail generation
95% reduced data entry rate with automation
Based on the outcomes achieved, the client entered into a 24/7 managed services agreement with us. This agreement covers:
Since 1999, SunTec has been a trusted business process and software engineering partner for technology companies worldwide. Our team of over 1,500 full-time employees delivers innovative services every day to help our clients save time and money.
