Picture this: a fraudster puts a picture of his victim's face on a dummy, opens a banking app where the victim has an account, and manages to bypass facial recognition software to fraudulently apply for bank loans. It's like something out of a bad spy movie, but unfortunately, it's a real-world example of the growing threat of identity fraud.

Little wonder then that in today's digital age, online identity verification is becoming increasingly important for businesses and corporations. While facial recognition technology can be a powerful tool in verifying identities, it's not always foolproof. Just ask Robert Williams, who was wrongfully arrested based on a flawed match from a facial recognition algorithm. It's like the technology is saying, "Sorry, I thought you were someone else." That’s because traditional methods of identity verification can be time-consuming, prone to human error, and are often not scalable. This is where Artificial Intelligence (AI) and Machine Learning (ML) technologies come into play. AI and ML technologies are revolutionising security and user experience by improving accuracy, streamlining the onboarding process, and reducing manual work. They can prevent identity fraud by significantly improving accuracy during identity matching, reducing the need for error-prone manual processes and rapidly matching user identity with multiple government databases.

AI and ML technologies can automate many KYC processes, reducing the need for manual intervention, thus reducing the chances of human error.

Upgrading the KYC process through AI and ML
Conducting an identity check and completing the Know Your Customer (KYC) process is a crucial step in the third-party risk management process which financial institutions and companies undertake before entering into a transaction with counterparty. It is also a prerequisite to comply with regulators’ KYC and Anti-money Laundering (AML) regulations. AI-based OCR and face-matching technology embedded in modern KYC solutions have revolutionised identity verification. These solutions enable identity verification through live video-call, verification of identity documents against the Aadhar, PAN, and other statutory databases in India, facilitate live geo-tagging, provide real-time liveness checks of the individual being verified, and compare the video image of the individual with the photo in the identity documents. Video KYC is extensively used in the Banking, Financial Services and Insurance (BFSI) sector in particular.

Here are some ways in which AI and ML are transforming the KYC process:
● Improved Accuracy: AI and ML technologies can identify even subtle patterns and anomalies between the video image of the person whose identity is being verified and their identity documents on various statutory databases. This leads to more accurate and reliable results, reducing false positives and negatives.

● Streamlined Onboarding Process: AI-powered KYC systems can verify the authenticity of individuals' identities in real-time, enhancing the efficiency and effectiveness of the verification process, making it easier for businesses and financial institutions to onboard new customers quickly and efficiently, without compromising on security.

● Reduced Manual Work: AI and ML technologies can automate many KYC processes, reducing the need for manual intervention, thus reducing the chances of human error. This also helps in reducing manpower costs.

As businesses continue to grow and expand, the need for reliable and efficient KYC processes will become increasingly important, and clearly, AI and ML technologies are at the forefront of this revolution.

"ML is particularly effective in this regard, as it helps continuously improve the KYC systems’ detection capabilities with access to larger datasets over time."

Conducting Liveness Checks
Unlike traditional identity theft, liveness fraud involves perpetrators attempting to deceive biometric authentication systems by presenting fake or stolen biometric data while mimicking the "liveness" cues that indicate a live person. The fraudster mentioned in the opening paragraph of this article was attempting such a fraud by passing off the photo of the victim as a live person. Thankfully, AI offers a formidable defence against such fraud. By leveraging sophisticated algorithms, facial recognition systems can be trained to detect the subtle nuances that distinguish a live person from a fake one. They can analyse micro-expressions, eye movement, and even heart rate variability to ensure the presence of a genuine user. Furthermore, advancements in ML enable systems to learn from a vast dataset of liveness cues, improving their accuracy and adaptability over time. This powerful combination of AI and ML equips businesses with the tools to thwart liveness fraud, thus strengthening the integrity of the online identity verification process and safeguarding against malicious actors.

Combating the Problem of Synthetic IDs
Synthetic identity fraud refers to a form of identity theft where fraudsters combine genuine and counterfeit personal information to fabricate a new identity. This deceitful identity is then exploited for various fraudulent activities like credit card fraud or bank fraud. AI and ML can play a vital role in combating this type of fraud by utilising advanced algorithms and data analysis techniques to detect irregularities in data, thus flagging potential synthetic identities and preventing financial losses in a proactive manner. ML is particularly effective in this regard, as it helps continuously improve the KYC systems’ detection capabilities with access to larger datasets over time.

An effective approach to identifying synthetic IDs involves leveraging third-party data, recognising that genuine individuals leave behind extensive traces of their activities across multiple data systems, both physical and digital. These trails are difficult to replicate, possessing depth and a wealth of historical data spanning several years. In contrast, synthetic IDs tend to display inconsistencies because certain details provided by the applicant may be real (such as a name appearing across various data systems) while others are fabricated. It would be impossible for humans to detect such subtle frauds in the short time frames that are available for identity checks, but AI and ML algorithms can detect these and prevent any significant damage by the perpetrators.

AI and ML: The Future of Identity Verification
Recently the Union IT Minister Ashwini Vaishnaw announced that the Department of Telecommunications (DoT) used AI and facial recognition-based tools to weed out over 3.6 million fraudulent phone numbers. The tool, called ASTR, is an acronym for Artificial Intelligence and Facial Recognition powered Solution for Telecom SIM Subscriber Verification. This is a significant step towards preventing rampant cases of fraudulently procured SIM cards being used for financial and other cybercrimes.

Many such efforts are underway to tap the potential of AI and ML to weed out identity fraud because these cutting-edge technologies harness the power of pattern detection, network analysis, and behaviour analysis to fortify our defences against fraudsters. By leveraging advanced algorithms and analysing vast amounts of data, AI and ML empower organisations to proactively identify and mitigate the risks associated with identity fraud. The ability to swiftly detect anomalies, expose synthetic identities, and scrutinise suspicious networks is transforming the landscape of online identity verification. As these technologies continue to evolve and adapt, they hold immense promise in bolstering our digital security and fostering a trustworthy online environment.