Reducing the lead time and error rate in KYC verification process at financial institutions
Our project aims to automate the KYC process for financial institutions, namely Banks, Non-Banking Financial Companies (NBFCs),
and Payment Aggregators, by extracting data from customer submitted documents at application stage.
We aim to provide an INDUSTRY READY solution where the user uploads the required documents
We are eliminating the human dependence and manual intervention for submitted document’s verification.
Any anomaly noted through document verification will be informed to the user at application stage itself.
Turn around time of document verification will be reduced from days to not more than 30 seconds.
How are we solving this problem?
Using machine learning algorithms, we aim to automate processing of submitted customer personal information documents for KYC norms.
Using OCR, NLP and CV technologies, meaningful data can be extracted from uploaded documents.
Extracted data is processed further and matched to customer declared details through which an anomaly report is generated.
Known anomalies in submitted documents are communicated on real-time basis to uploader( consumer) and financial institutions.
Onus of completing KYC timely with correct documents will shift entirely to customers.
Tech Stack Used:
React JS : React will be used to design the website
Tailwind CSS : Tailwind CSS is used to provide styling to the website
FastAPI : FastAPI provides an intuitive and easy-to-use interface for building scalable and async web applications with automatic interactive API documentation and validation.
Computer Vision : This field of AI will be used for image classification and image processing
OCR : Optical Character Recognition will be used for extracting text from the documents
Deep Learning : Deep Learning will be used to create state of the art classification models for aur custom image classification problem.