Have you ever thought that a signature forgery detection software would be able to distinguish between fake, forged and genuine signatures? Well, forgery detection is indeed possible today, thanks to the growth of Artificial Intelligence enabled identity management systems.
An AI-enabled system can smoothly segregate false signatures from documents and enable stakeholders to validate their identity for compliance purposes. So why is there a need for such identity management systems?
Let us examine the backdrop. Over the years, industries and governments across the world are increasingly facing newer forms of fraudulent threats from all sides. In a growing economy like India alone, the direct cost of financial fraud is more than 20 Billion USD annually. You could only imagine the scale on which such frauds happen globally.
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Signature forgery and impersonations are causing major breakdowns in policies and legal compliance measures as money and benefits are channeled to the wrong personnel causing severe strain on economic resources and ultimately impact the economy as a whole. One of the key areas of fraudulent practices that many regulatory bodies are struggling to cope with is fake signatures.
Use of fake signature is a major issue that has been plaguing numerous sectors globally. From banks to insurance providers to government authorities, there have been numerous cases wherein the signature of the correct authority is forged to obtain illegal benefits. Even artistic works have suffered greatly due to signature forgings, wherein original art producers have been cheated for due credits by impostors who forged their signatures. This has over the past led to numerous copyright infringement litigation and not to mention rampant abuse of royalty rights and much more.
Recently we have worked with a prominent bank in Indonesia to build a solution for signature forgery detection. The AI-powered fake signature detection software was able to reduced the cheque signature verification time by 70%. You can read the case study here : Bank Process Automation Case Study
We are all familiar with optical image recognition tools that are available today that deal with recognizing characters and patterns for an easy image to textual translations. AI enables systems to take this process to the next level wherein they are able to learn textual patterns in scanned images or in actual document visuals. This technology can be very helpful in detecting signature forgery and fake signatures.
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The signature forgery detection software uses deep learning algorithms to compare it with the original signature to identify even the minutest variations. But there are situations wherein even the original signature owner might fail to reproduce 100 percent actual replica of his or her signature.
AI systems are cleverly engineered to identify and measure mismatch percentage and determine whether it is, in fact, a genuine signature that has a minor difference from the original one. This greatly improves the reliability of such systems as it does not cause inconvenience for genuine signatories.
The AI enabled Identity Management and Verification system for signatures finds use in a number of domains but let us shed light on some core areas for now:
Verification of customer signature for approval of loans, disbursal of cards and other related information can be made easy by cross-examining and comparing with originally approved signatures within bank records. This is especially beneficial in preventing fake signatures on cheque leaves and withdrawal documents which would reduce financial fraud to a great extent.
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The insurance sector is a major beneficiary of AI enabled signature verification systems since it relies primarily on documents submitted during policy enrollment to verify claims accordingly. Any signature mismatch or fraud can lead to the benefits being given away to illegal claimants and can cause severe implications.
Medical records or referrals from doctors are often faked in order to gain illegal access to patient info or for submission to fraudulent insurance claims. Hospitals and healthcare companies find it difficult to detect anomalies in the signature on claim or referral documents since they do not appear differently when brought into their notice. AI enabled systems can easily identify if fake signatures have been used and hence save the considerable amount of time and money for the service providers.
There have been cases related to royalty payments on artistic work like music paintings and other art forms. With AI enabled signature verification systems, these cases would have legal backing to identify the exact origin of the signature and hence artistic royalty payments would not face any issue.
Signature verification on age-old government files and regulatory documents are often a painstaking experience for everyone involved be it citizens or government officials. With the humongous amount of files to hover through, there are chances where wrong signatures could approve government benefits to undeserving people while denying it to the right beneficiaries. With AI-powered systems, this saga is totally avoided and beneficiaries can easily gain access to their rightful claims.
AI enabled signature forgery detection software can greatly improve efficiency in sectors that deal with a large volume of paper works that involve signatures. It saves time and effort and eliminates fraudulent practices to a great extent.
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While you might be wondering about the massive investments that may be needed to set up AI enabled fake signature detection systems, it is important to know that there readily available AI API services that easily integrate with your current file management and document control systems and would do the verification job quite easily with minimal systems integration requirements. If you would like to know more about such systems, feel free to drop us a line and our consultants will get in touch with you shortly to help you identify how you can improve document security with AI enabled signature verification systems.
Signature forgery detection using AI applies deep learning algorithms to compare the original signature to identify even the minutest variations.
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