Automated Financial Documents Processing System Using Machine Learning

In today’s world technology has affected each sector. But still, within the banking sector, we tend to face several problems. Bank handles giant volumes of cheques within the clearing method. The method involves several technical verifica- tions as well as signature verification. A number of these steps are manual and need human intervention to finish the technique. This method needs high human capital preparation and longer interval. Fallacious practices are one in every one of the foremost problems plaguing banks in recent times. Hiring a lot of folks to manage the cheque verification method does not solve this issue. This project helps as an answer to the present downside. Optical Character Recognition (OCR), workflow systems, and machine learning techniques are the key technologies to building automatic document processing. When it involves the clearance of bank cheques and financial transactions, this alternate methodology for the process of bank cheques with stripped-down human intervention additionally saves time, and automating the method through computer vision technology can facilitate ensuring that solely authentic banknotes are handled and under such scenario, cheques will not be delayed in reaching its destination. We tend to propose an automated system that extracts relevant details of a bank cheque like Payee Name, Amount, Date, and Bank Name using Optical Character Recognition and Deep Learning and verifies the signature on the cheque with the prevailing signature keep within the database using feature extraction and principal component analysis. Our innovation aims to learn the banking industry by re-innovating the other competent cheque- based financial transaction system that needs machine-driven system intervention.