Signatures are seen everywhere - in art, cryptography, music, bank cheques, etc. It is a mark of a person for a proof of identity and intent. It is distinctive and unique to that person. It’s also a way to confirm the person's identity. Although it is unique and private to that person, it can be forged. Banks use the signature to verify the cheques, cryptocurrencies use the digital signature to verify a transaction, art museums use the signature to identify the art’s owner. Hence, identifying the authenticity of the signature is of utmost importance. This is where the automated signature validation becomes important.
In the past, people used to manually do signature validation. Wherein, the bank’s employee would authenticate the signature on the cheque, art museum’s employee would authenticate the signature on the art and so on. This requires a good eye to notice any subtle differences and is prone to error. Also, it is quite a time-consuming process. With the advent of the latest technology with incredible capabilities, this process is now automatized. It reduces the errors and also fastens the process. Nowadays, where billions of transactions occur on a daily basis, it is imperative that this process is fast and seamless.
There are multiple ways to automate the process of signature validation. The traditional way is to obtain the image of the signature. Then use image processing techniques such as Gray conversion, Noise reduction, Edge enhancement, binarization, and others to get the signature in a good format. Then, extract features from the processed image of the signature. High level features such as width, height, aspect ratio. Low-level features from specific parts of the signature such as the count of the transition from black to the white pixel and vice-versa. Then store these features to compare it with the signature that needs to be verified. While there are other approaches such as using Fuzzy models, Hidden Markov models and others, the current best approach is to use Deep Learning. Deep Learning has done wonders with images and it outperforms all the other approaches currently known.
The human resources required to process and verify the innumerable transactions that occur on a daily basis is no longer an option. The automation of signature validation is not just to verify but to detect fraud. Automated Signature validation is a solution for efficient and fast validation of the signature.
FAQs
A pre-fund QC checklist is helpful because it ensures that a mortgage loan meets all regulatory and internal requirements before funding. Catching errors, inconsistencies, or compliance issues early reduces the risk of loan defects, fraud, and potential legal problems. This proactive approach enhances loan quality, minimizes costly delays, and improves investor confidence.
A pre-fund QC checklist is a set of guidelines and criteria used to review and verify the accuracy, compliance, and completeness of a mortgage loan before funds are disbursed. It ensures that the loan meets regulatory requirements and internal standards, reducing the risk of errors and fraud.
IDP (Intelligent Document Processing) enhances audit QC by automatically extracting and analyzing data from loan files and documents, ensuring accuracy, compliance, and quality. It streamlines the review process, reduces errors, and ensures that all documentation meets regulatory standards and company policies, making audits more efficient and reliable.
Yes, IDP uses advanced image processing techniques to enhance low-quality documents, improving data extraction accuracy even in challenging conditions.
IDP efficiently processes both structured and unstructured data, enabling businesses to extract relevant information from various document types seamlessly.
IDP combines advanced AI algorithms with OCR to enhance accuracy, allowing for better understanding of document context and complex layouts.
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