Artificial intelligence is especially potent in document authentication because its machine-learning algorithms are able to identify small details and discrepancies that humans are generally unable to with the naked eye.
These are some examples of the roles played by artificial intelligence during document authentication processes:
AI-powered systems are able to scan and analyse various properties of a document — such as text, images, fonts, layout, and even metadata. If any discrepancies are detected, the system will quickly flag suspicious documents for further review by comparing their contents against known patterns and standards. This not only accelerates the authentication process but also reduces the risk of human oversight.
Optical Character Recognition enables document authentication systems to recognise and extract text from images or scanned documents. In the context of authentication, OCR is used to detect discrepancies between the visual content of a document and its textual representation. This helps to identify tampering or forgery that might not be immediately apparent to the human eye.
Signature verification scans through the subtle nuances and characteristics of signatures, such as stroke patterns, pressure points, and speed. AI algorithms can determine whether a signature is genuine or a forgery by comparing an individual’s signature to their known copy in shared storage.
Machine learning allows systems to learn from large datasets and identify intricate patterns. AI algorithms become better at recognising the subtle cues that differentiate genuine documents from fraudulent ones as they process more and more documents. This adaptive learning process ensures that AI-powered document authentication systems continuously improve their accuracy over time.