by
August 29, 2022
Before cell phones, high-speed internet, and a global pandemic pushed the globe to digital-first onboarding, banks, financial institutions, and other organizations could verify identities by matching a person's face to an actual picture ID. The verification could not be accepted at face value; pardon the pun. A back-end system was still required to confirm the identification by comparing unique identifiers, such as name, date of birth, and address, to independent credit header and utility data.
Document verification is the process of confirming a document's legitimacy. Typically, the most recognized and verified papers are a driver's license, a bank statement, or other state or federal records. The method demonstrates document characteristics such as stamps, watermarks, typefaces, and transport materials. Additionally, PII information from the front of the ID is compared to the same information collected from the machine-readable zone (MRZ) on the back of the document.
With digital onboarding, candidates no longer need to be physically present to authenticate their papers or the facial comparison with their picture ID. The applicant uploads a picture of their government-issued ID and a selfie using an image capture application on their smartphone. A document verification procedure includes identification documents such as a passport, national ID card, state government documents, utility bills, and tax returns. The verification technique considers papers including an authentic user picture so that a failsafe identity verification can be performed and the possibility of impersonation is removed.
The document verification process examines the document's characteristics, such as stamps, watermarks, holograms, typefaces, and other security elements, and then automatically approves. This automation saves time and resources for the firm and allows staff to deliver better services.
Identity theft online is a significant issue for all internet companies.
In the United States, 47% of people have suffered financial identity theft. In 2019, identity theft resulted in losses of $502.5 billion, which grew by 42% to $712.4 billion in 2020. Losses are projected to rise to $721,3 billion in 2021.
Document verification is a vital part of the client identity verification process. Online document verification is required for compliance with KYC rules and account opening. During the onboarding of new customers, document verification may assist firms in verifying new customers swiftly and securely by examining the legitimacy of identification papers.
Online document verification software, such as VisionERA reduces client onboarding time to minutes. With AI-enhanced OCR software, teams can carefully extract data from ID papers and merge it with their current CRM software to generate comprehensive client profiles.
The document verification solution requires the user to upload a photograph of their identification document using their webcam or smartphone camera. The back-end system gets user input in the form of an image of the paper, including their picture. The user also sends a selfie of their face for further identification verification.
The user contributions are subjected to several tests to establish whether the information is authentic, and the document has not been forged, hacked, or changed. The picture on the paper and the selfie uploaded by the user are then compared to determine whether they are comparable.
Upon successful review of user credentials, the customer is informed of the findings of the validation of user identification. Various characteristics, including holograms, rainbow prints, MRZ codes, document edges, tampering, and photoshopped content, are used to verify the validity of a document.
The key to authenticating physical documents and performing face comparisons in a digital environment depends on machine learning and artificial intelligence automation. This implies that the composition and selfie may be subjected to hundreds of validation tests in seconds instead of a simple visual inspection by an inexperienced eye. The sophisticated picture capture program instructs the user on how to shoot high-quality ID documents and selfie images by removing blur, glare, and other difficulties, increasing the possibility that a good user will pass security checks on the first attempt.
Among the validation tests conducted by Socure's automated identification document verification solution:
Machine learning (ML) approaches need large datasets to achieve high accuracy, and the amount of training data is one of the primary factors of a model's predictive ability. Machine learning models use these data for training and continuous learning, which compound performance. Socure’s supercharged machine learning categorization is trained on 700 million "known good" and "known bad" identities and reinforced with data from over 400 third-party sources. The following are the advantages of machine learning for document verification:
Today's customers demand an onboarding process with little friction and fast account access. As document verification grows more prevalent, requiring a head-turning video or numerous selfies creates an unpleasant scenario that risks customer desertion and harms the image of your company. When 84% of customers believe that a good onboarding experience is just as essential as a company's goods and services, organizations must provide a contemporary approach to document verification to satisfy user expectations. Using hundreds of multi-dimensional predictive signals on top of document authentication, liveness detection, and enhanced facial biometrics to identify more good customers and eliminate fraudsters in real-time, Socure's Predictive DocV provides a holistic, machine learning-powered decision in seconds with 98% accuracy using hundreds of multi-dimensional predictive signals.
These documents are entirely fraudulent, as they are not issued by an official government agency and lack official stamps, holograms, and signatures.
These materials belong to a third party and may have been stolen physically or accessed electronically. Fraudsters attempt to circumvent the system using these papers.
The papers that are legitimate but have been altered or photoshopped, such as the date of birth or address being changed without official authority.
The user's information is validated by comparing the information on the identification document with the information the user has given. This facilitates the elimination of potential cases of identity theft by internet enterprises.
Automated Identification Manual user identity verification has become obsolete. Automated operations may save substantial money and time with digital document verification of the user’s identification.
Online document verification assists companies in preventing online fraudsters and identity thieves from misusing the identities of their consumers and excluding them from their platforms.
Incorporating intelligent methods to evaluate if the onboarding user is a possible danger or a secure consumer, remote customer identity verification utilizing document authentication is flawless.
Know Your Customer (KYC), and Anti-Money Laundering (AML) compliance may be achieved by document verification, which is beneficial for avoiding future cybercrime.
Verifying identification papers digitally may allow age-exclusive service providers to confirm that their consumers are over the relevant age restriction.
Which Industries will gain?
Verify that the security features are in the correct location.
Each identification has security characteristics, such as watermarks, logos, holograms, and microprint. Verifying these security elements are where they should be is an excellent first step in authenticating a document. Only a clear picture of the record is required to validate its security features. The final document picture should be crisp and horizontally aligned.
Examine the ID data for coherence
Whether in a barcode or a Machine Readable Zone, all identification documents include encoded personal data (MRZ). Any discrepancy between this information and the information written on the remainder of the document (the so-called Visual Inspection Zone, or VIZ) indicates document alteration.
Match the user's face with the document's picture
A further method of validating a document is to compare the customer's ID photo and uploaded selfie. If the features on these two photographs match, the user is the legitimate owner of the uploaded document.
The design of technologies and procedures that facilitate the generation of electronic documents is document process automation. These include logic-based systems constructing a new record from pre-existing text and/or data parts. In many businesses, this method is used to compile legal documents, contracts, and letters. Automation solutions enable enterprises to decrease data input, reduce the time spent proofreading, and eliminate the chance of human mistakes.
The document verification procedure is straightforward; data is acquired from clients’ sources, and selected information is retrieved from the relevant papers. Multiple AI checks assess and verify the articles’ legitimacy, reducing identity theft. And lastly, the document is validated after passing through a human-in-the-loop procedure. Below, we will examine all the processes in depth.
In the first phase, the necessary paperwork is submitted. Customers must provide high-quality images or videos of their valid identification papers. Artificial intelligence and algorithms scan the document for information, even encrypted data, and identify it.
In the first stage, documents are preprocessed and ready for the subsequent step. This comprises inspecting the boundaries of the documents, de-skewing the photos, and enhancing the image's color and brightness so that data extraction in the subsequent phase is enhanced.
The second stage involves carefully extracting the needed information from the papers. Using sophisticated document processing software, users may choose the pertinent information and eliminate any other extraneous data.
It is advisable to use authentic OCR software since it turns your complete document into a structured, informative document that can be fed straight into your subsequent data sources. At this stage, you may also categorize papers based on their templates and verify that all customer documents are adequately documented.
Validating the data from scans necessitates determining if the document and the displayed information are still legitimate. You may confirm the correctness of the retrieved information against any data source using the extracted data. For instance, the loan processing department may want to determine whether the applicant has any existing loans with the same bank. To decrease identity theft and fraud, it is crucial to validate documents.
Among the validation tests performed by an automated identification document verification system are the following:
In the last stage, papers that have been highlighted for whatever reason undergo an approval procedure. This combination of manual and automated processes is called the human-in-the-loop verification procedure. After a thorough examination, the papers are then confirmed.
Businesses that enjoy a reasonable amount of success due to their development and the popularity of their ideas must combat the complicated network of fraudsters constantly targeting their online portals. Using AI for ID verification boosts the security of commercial outlets by a factor of 10 and gives companies a competitive advantage against fraudsters. When combined with human intelligence, AI enhances the human capacity to analyze data, allowing organizations to process information intelligently and avoid fraud. Among the advantages that machine learning provides to document verification are the following:
Instead of months or years, automation document processing solutions may be installed and taught in days or weeks. Organizations with a cheaper total cost of ownership may use automated document processing for various documents to obtain a return on investment (ROI) more quickly than with conventional solutions. Platforms such as VisionERA may give a higher ROI on this in a short span of time.
AI with deep learning offers fast document modeling and training, allowing new, never-processed documents to be rapidly incorporated into the document processing system. With decreased lead times, businesses may react rapidly to new possibilities by developing new document processing apps without adding personnel.
Automation fueled by AI may allow "human-in-the-loop" validation, which provides document categorization and extraction findings to the person who submitted the document. This permits rapid and prompt verification and correction of information in near-real-time. While collaborating with customers, partners, or other external stakeholders, frontline employees may swiftly make mistakes and seek further information. This saves time and improves data accuracy and transparency for all business process participants.
Using Photoshop, EXIF, or forgery detection, AI-enabled document processing software may identify bogus documents. It monitors the papers for any anomalies that might suggest manipulation.
Document verification software automates the whole document verification procedure, standardizing and scalability it across all geographic regions.
With an automated onboarding procedure, the client onboarding time may be reduced from days to minutes. VisionERA may improve client onboarding by reducing waiting times and increasing accuracy to almost double.
Using AI-enhanced IDP technology, document verification platforms such as VisionERA extracts information from client papers. With no human interaction save for situations that have been highlighted, the overall process accuracy rises manifold.
This is the optimal option for banks and non-bank financial organizations seeking to cut costs, effort, and errors while increasing the client experience. Verify users within 30 to 60 seconds to increase conversion rates and decrease drop-offs. This technology allows immediate and real-time document verification, such as bank account verification, bank statement verification, proof of address papers, tax return verification, and identity verification.
Organizations are inundated with papers. Consequently, document processing has a tremendous influence on paper-intensive procedures. Automation software can automatically extract and arrange data across sectors and corporate operations.
In addition to the document overload, many enterprises are subject to stringent KYC and AML compliance regulations in financial services, banks, and insurance companies. Using capabilities such as IDP for document verification helps lessen the burden of lowering customer response time and increases customer satisfaction. Other than this, every organization
The demand for internet services and goods is expanding at a pace that is unparalleled. With this move to online platforms, it is imperative that companies validate the identification of their clients using an online portal. Online document verification is essential for organizations to maintain safety, prevent service abuse, and improve customer satisfaction. For instance, ride-sharing applications need online document verification to confirm that both riders and drivers are actual people. Online document verification is necessary to combat online identity theft and improve online security.
VisionERA is an AI-enhanced, intelligent document processing platform that enables businesses of all sizes to scan, categorize, and selectively extract vital information from their documents. Organizations use it for comprehensive document management. Enterprise customers utilize them extensively to undertake Passport Verification, ID Verification, Driver License Verification, Invoice Verification, Vendor Verification, Accounts Payable Automation, and more on a secure online platform. It is a complete DIY platform which allows users to use default Doc config/Workflow config for experimentation.
Besides this, VisionERA IPD comes loaded with an array of features such as Deep Learning models, One-shot Retraining, Continuous Learning Mechanism with Feedback for OCR Retraining and Unstructured Content Retrieval. Backed by a powerful recommendation engine for better sampling of feedback files, it gives users an ability to sample failed cases at the click of a button besides having multiple features that can help organizations undertake verification of documents at scale effectively and efficiently.
To know more about how VisionERA can do this, book a demo with us now.