Fintech expert shares 5 ways AI can help detect deepfakes in eKYC systems
Oct 9, 2024, 16:23 IST
The recent PayTM saga exposed how financial institutions are finding it increasingly difficult to maintain sufficient security measures to ensure user authenticity and identity. Now, many fintech platforms are turning to artificial intelligence (AI) to address these challenges. A leading voice in this conversation is Rahul Ayyappan, the Co-Founder and Chief Technology Officer (CTO) of Finacus Solutions Pvt. Ltd.
With over a decade of experience in shaping digital solutions for over 200 banks and financial institutions, Rahul sheds light on how AI can play a transformative role in addressing the issue of deepfakes and even strengthening eKYC (electronic Know Your Customer) systems.
Facial recognition technology
AI algorithms can analyse facial features in-depth, detecting subtle inconsistencies that are often overlooked by human eyes. By examining dermal texture, facial feature depth, and lighting uniformity, AI can identify discrepancies indicative of deepfakes. Furthermore, AI can analyse the temporal consistency of facial movements in videos, identifying irregularities that may signal tampering.
Behavioural analysis
AI's ability to analyse micro-expressions, those brief, involuntary facial expressions that reveal genuine emotions, is crucial in detecting deepfakes. These expressions are difficult to replicate in fabricated media, making them a reliable indicator of authenticity. Additionally, AI can evaluate gait and gesture analysis, identifying inconsistencies that may suggest deepfake manipulations.
Consistency checks
AI can cross-reference data from various sources, such as document scans and live video KYC sessions, to identify inconsistencies in age, gender, or other demographic information. Geospatial validation can also be used to verify if the location of the eKYC session aligns with the user's stated address.
Machine learning models
AI models can be trained on vast datasets of real and fake images and videos to learn to identify patterns and anomalies associated with deepfakes. These models can continuously adapt and improve their accuracy as new deepfake techniques emerge.
Integration with ancillary technologies
Combining AI with other technologies, such as blockchain and thermal imaging, can further enhance deepfake detection capabilities. Blockchain provides an immutable ledger of verified data, while thermal imaging can detect heat patterns on the face that are difficult to replicate in synthetic media.
By leveraging these advanced AI techniques, eKYC systems can significantly improve their ability to detect deepfakes and protect against identity theft and other fraudulent activities. As deepfake technology continues to evolve, AI will play a crucial role in ensuring the integrity and security of digital identity verification processes.
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With over a decade of experience in shaping digital solutions for over 200 banks and financial institutions, Rahul sheds light on how AI can play a transformative role in addressing the issue of deepfakes and even strengthening eKYC (electronic Know Your Customer) systems.
Facial recognition technology
AI algorithms can analyse facial features in-depth, detecting subtle inconsistencies that are often overlooked by human eyes. By examining dermal texture, facial feature depth, and lighting uniformity, AI can identify discrepancies indicative of deepfakes. Furthermore, AI can analyse the temporal consistency of facial movements in videos, identifying irregularities that may signal tampering.Behavioural analysis
AI's ability to analyse micro-expressions, those brief, involuntary facial expressions that reveal genuine emotions, is crucial in detecting deepfakes. These expressions are difficult to replicate in fabricated media, making them a reliable indicator of authenticity. Additionally, AI can evaluate gait and gesture analysis, identifying inconsistencies that may suggest deepfake manipulations.Consistency checks
AI can cross-reference data from various sources, such as document scans and live video KYC sessions, to identify inconsistencies in age, gender, or other demographic information. Geospatial validation can also be used to verify if the location of the eKYC session aligns with the user's stated address.Machine learning models
AI models can be trained on vast datasets of real and fake images and videos to learn to identify patterns and anomalies associated with deepfakes. These models can continuously adapt and improve their accuracy as new deepfake techniques emerge.Integration with ancillary technologies
Combining AI with other technologies, such as blockchain and thermal imaging, can further enhance deepfake detection capabilities. Blockchain provides an immutable ledger of verified data, while thermal imaging can detect heat patterns on the face that are difficult to replicate in synthetic media.By leveraging these advanced AI techniques, eKYC systems can significantly improve their ability to detect deepfakes and protect against identity theft and other fraudulent activities. As deepfake technology continues to evolve, AI will play a crucial role in ensuring the integrity and security of digital identity verification processes.
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