AI and Blockchain Synergies Reduce Risk of Deepfakes in KYC

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Compliance with anti-money laundering (AML) and know-your-customer (KYC) laws and regulations is often a top concern for digital asset companies. Despite fraud monitoring and detection efforts, digital asset companies face significant challenges due to advances in technological threats, including generative AI.

Gen AI can generate highly realistic deepfakes, fake documents, and create a compelling life story almost instantly to support fake information, for example, false posts to a person's social media accounts. By seeding from For example, fraudsters recently used deepfake technology to simulate a video conference involving the CFO and other executives of a multinational financial company, and dupe an employee out of about $26. Moved million scammers.

In short, gen AI's ability to produce compelling deepfake audio and visuals almost instantly could wreak havoc on existing governance systems designed to protect consumers.

Current KYC mechanisms are inadequate with the advancement of general AI.

AML and KYC programs are essential for financial institutions to verify the identity of their customers and ensure compliance with laws designed to fight money laundering, fraud and terrorist financing. However, many crypto companies have weak or insecure KYC controls, which increases the risk of fraud. According to Coindesk, crypto users lost about $4 billion to “scams, rig-pulls and hacks” in 2022 and about $2 billion in 2023.

Since digital asset companies typically do not have physical locations like traditional financial institutions, they need to use KYC methods adapted to remote environments. Commonly used KYC verification methods include:

  • taking a selfie holding a handwritten sign with the current date;
  • take a picture of the User's driver's license or other official ID; or
  • Recording live video of the user answering security questions to verify their identity and “liveness”.

However, gen AI can bypass these existing authentication methods. For example, services like OnlyFake use AI to create fake IDs that have reportedly passed strict KYC checks at major cryptocurrency exchanges like Binance and Coinbase. These fake IDs are generated using neural networks and can be purchased for as little as $15. Deepfake offensive toolkit or Dot The virtual camera creates deepfakes for injection and allows users to replace their face with an AI-generated face to pass identity verification. According to this article in The Verge, financial institutions' KYC identity verification tests, which typically require a user to look at their phone or laptop camera, are easily fooled by deepfakes created by them. . Dot.

Using Gen AI in conjunction with Blockchain can reduce fraud enabled by Gen AI

Blockchain and AI are complementary technologies that can be effective for fraud detection and investigation, both independently and when combined.

Blockchain for validation

Decentralization, immutability and rule-based consensus are some of the key features of blockchain technology that make it useful for identity verification and fraud detection. For example, transactions written on a blockchain are immutable (ie data cannot be deleted or changed), which can prevent fraudsters from altering transaction data. Additionally, transactions recorded on public blockchains, such as the Bitcoin blockchain, are fully traceable and transparent, making fraudulent activity difficult to detect. Blockchains are also distributed in nature, making it more difficult for a single entity or small group of entities to make unauthorized changes to data on the blockchain. Finally, data on blockchains can be hashed cryptographically, creating a unique digital fingerprint that is nearly impossible to recreate. This feature helps in tracking fraudulent transactions as the hash value will also change if someone tampers with the data on the blockchain.

AI to detect:

AI can enhance fraud detection by analyzing user behavior patterns and identifying anomalies in real time. Unlike blockchain technology, which is useful for auditing past transactions, AI can learn and adapt to potentially fraudulent behaviors in real time. For example, advanced AI detection algorithms can analyze user behavior patterns and identify anomalies, flagging suspicious activity that deviates from normal usage. AI can quickly sift through mountains of data and spot subtle inconsistencies that often escape human detection. Machine learning models and AI-powered behavioral analysis enable AI to analyze user interactions such as mouse movement patterns and typing patterns, providing an additional layer of identity verification on top of blockchain. can increase AI's ability to proactively monitor and detect fraud and blockchain's ability to verify user identity and transaction validity are a powerful combination.

As general AI evolves, there is an urgent need to develop solutions.

Crypto-related cybercrime is only increasing as AI deepfakes become more reliable and realistic. However, in the face of this growing threat, several startups have developed AI-centric blockchain tools to fight fraud and other illegal activities in the digital assets industry.

For example, BlockTrace and AnChain.AI are two companies that are leveraging the convergence of blockchain and AI technology to fight crypto-related crimes. BlockTrace's, whose mission is to help governments and private institutions fight cryptocurrency-related financial crimes, recently partnered with AnChain.AI, a company that fights fraud, scams and financial crimes in digital assets. Uses AI capabilities to BlockTrace and AnChain.AI will provide solutions to allow national security agencies to use AI to investigate smart contracts, provide intelligence on blockchain transactions, and provide cybersecurity insights to national security authorities. be given

The industry is very close to fully utilizing the potential of AI and blockchain to combat AI-enabled fraud, and given the rapid pace at which AI is advancing, many more developments are still to come. .

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