Machine Learning for Document Processing in Finance

Manual document processing caused inefficiencies and errors for the client, hindering digital transformation and compliance. Most valuable information was in unstructured formats, affecting their adaptability. The client sought a machine learning-based solution to automate data extraction, enhance efficiency, and improve compliance. The aim was to streamline workflows, reduce processing time, increase accuracy, and provide reliable services, enabling them to utilize data effectively, accelerate digital transformation, and stay competitive in the finance sector.

We developed a machine learning-based solution for document processing, incorporating: OCR for digitizing documents and faster data extraction; automated data extraction, eliminating manual input; ML-driven data validation through cross-referencing and consistency checks; document classification and categorization using ML models; automated review and approval processes, ensuring compliance. The solution prioritized data security, employing encryption, secure authentication, and access controls to protect sensitive information.

The ML-powered solution achieved over 99% accuracy and reduced operational costs by 60-70% through automation. It boosted productivity by integrating with existing systems and automating data handling. Processing time was cut to 30 seconds for near-instant data processing, and collaboration was improved via cloud storage and streamlined data collection.