In the last few decades, companies have been rapidly moving to achieve complete digital transformation. It involves large-scale processing of consumer data. Although the aim is to enhance business operations and customer journeys, it has also resulted in a surge in document fraud. Companies find it highly challenging to curb such cases using traditional verification methods. However, innovations in advanced technologies, such as artificial intelligence and machine learning, have enabled the development of robust document forgery detection tools to enhance security. This post discusses the common types of document fraud and how document forensic analysis catches alterations in them.
Scammers have various reasons to use document fraud, but the result is that it can cause a loss of billions of dollars to the global economy. These risks are higher in financial institutions, such as banks and nonbank financial companies (NBFCs), as they must conduct Know Your Customer processes during customer onboarding. In 2020, Consumer Sentinel Network reported that the Federal Trade Commission in the US received over 2.1 million document fraud cases. On the same note, there were more than 4.7 million identity theft cases in the same year. The report noted that the most prevalent document fraud cases were related to impostor scams.
Forensic document examiners report that it’s not just financial industries that are exposed to these risks. Instead, all high-value sectors that deal in large transactions face these obstacles and want to enhance their internal tools for document forgery detection. For instance, apart from the banking industry, real estate is at risk of identity theft and document fraud. Whether it’s the creation of fake sale deeds, ownership agreements, or property transfer documents, these cases hurt the industry at large and scare away potential buyers from entering the market. Therefore, business leaders and decision-makers need to understand the common types of document fraud and how to mitigate them using document forgery detection.
According to Identity Theft and Fraud reports of 2022, 422 million people were impacted in a single breach, with credit card fraud constituting 43.7% of total identity theft cases. Considering this fact, it becomes imperative to understand how many types of document fraud a document forgery detection tool should mitigate. Here are some common types of document fraud to look out for:
These documents are initially authentic, however, scammers tamper with them to completely or partially alter the information. This way, these documents can be used in impostor scams and are much harder to catch using traditional document forgery detection tools.
Unlike altered documents, counterfeits are unauthorized reproductions, and scammers make these documents from scratch. Due to easy access to advanced technology, criminals can produce highly intricate documents that can slip through the prevalent methods of document forgery detection.
This rare form of document forgery aims to produce documents designed to look like real ones, such as passports. However, camouflage documents are usually issued to nonexistent entities or in the name of nonexistent countries. This is why upgrading the document forgery detection system is essential to catch these rare cases.
Vendors can send fake invoices to companies to extort money from them, which can be difficult to catch if the business deals with many invoices. Therefore, companies must implement a reliable system to continuously monitor incoming invoices before disbursing the funds.
Traditionally, manual methods are used for document forensic validation. However, these processes need to be revised as scammers can use advanced technology to create forged documents that cannot be validated using manual document forgery detection. In such circumstances, it becomes vital for companies to implement a modern AI-powered document forgery detection tool. These solutions use optical and intelligent character recognition to extract data from documents accurately. It is then cross checked against external databases to ensure the utmost accuracy in the documents verification process. Similarly, data extraction enables the system to look for duplicate documents in its record and check for any data discrepancies among the provided documents. Moreover, the AI is trained to check document security features like watermarks, microprint lines, and rainbow printing. Therefore, even if the document is forged by modern tools, the errors will be caught in the forensic document exam.
Companies have traditionally relied on a manual workforce for document forgery detection. However, the subtleties of modern forgeries and alterations make it impossible for human eyes to detect the changes. Therefore, decision-makers must look toward modern AI-powered solutions to revitalize their document forgery detection process. Building such a solution from scratch can be time-intensive and take up a lot of resources. To save costs and increase the efficacy of this process, companies should look for a third-party document forgery detection tool.