X Space #21 – AI-based Predictive Fraud Detection in Web3 – why do we need this?
AI-Based Predictive Fraud Detection in Web3: The Missing Key to Mainstream Adoption
Web3 is suffering from high fraud rates and low user trust. Traditional static, rules-based systems fail to prevent scams and rug pulls. What’s the solution? AI-driven dynamic fraud detection. Just as Web2 overcame early fraud issues with real-time, AI-driven transaction monitoring, Web3 must follow suit. By predicting fraudulent behavior before it happens, Web3 can create trust, attract users, and thrive.
1. Introduction
Welcome to XSpace #21, where Martin and Tarmo, co-founders of ChainAware, delve into the crucial topic of AI-based predictive fraud detection in Web3. In previous discussions, they have explored how AI-driven AdTech can reduce customer acquisition costs. Today, they focus on the other side—why robust fraud detection is essential for user trust and ecosystem growth.
2. The Lessons from Web2 Fraud Prevention
Web2 didn’t start as the seamless online shopping paradise we know. In its early days, fraud was rampant, credit card theft was common, and users hesitated to complete financial transactions online. This distrust slowed adoption.
How did Web2 overcome this hurdle? Financial institutions invested heavily in AI-based fraud detection. They used dynamic, continuously evolving algorithms to identify fraudulent patterns before transactions cleared. Over time, this approach lowered fraud rates and built user trust, making Web2 mainstream and reliable.
3. The Web3 Fraud Challenge
Today, Web3 faces a similar challenge. Despite offering innovative solutions like DeFi, self-custody, and permissionless finance, Web3 experiences high fraud rates:
Rug Pulls: Up to 95–98% of new PancakeSwap pools end in rug pulls. On some platforms, the rate is even higher. New users often lose everything in these scams, tarnishing the ecosystem’s reputation.
Impersonation and Social Engineering: Hackers and scammers frequently pose as service providers or experts, targeting unsuspecting users who don’t know who to trust.
High Hackers’ Fee: Around 2–3% of the total value locked (TVL) in DeFi gets stolen annually, a number that has not significantly decreased over time.
These problems mirror those of early Web2. Users who enter Web3 without proper protection are often burned by fraud and leave, taking their liquidity and trust with them.
4. Why Static Analysis Tools Aren’t Enough
Current Web3 anti-fraud measures often rely on static rules:
Crypto AML (Anti-Money Laundering): Tools track the flow of “bad” funds but are easily bypassed through mixers and complex routing.
Smart Contract Audits: While audits verify contract code at a given point, they cannot predict how a malicious actor will exploit dynamic, ever-evolving environments.
Because bad actors are innovative, static rules are quickly outdated. Just like early antivirus software in the PC era, which relied on matching known signatures and failed against polymorphic viruses, Web3 needs a more adaptive approach.
5. AI-Based Predictive Fraud Detection: A Game Changer
AI-driven fraud detection is dynamic, pattern-based, and continuously learning. Instead of checking a few static parameters, AI looks at historical behavior, transaction patterns, and evolving liquidity flows. It answers the critical question: What will this address or contract do next?
Learn more: Transaction Monitoring Agent
Key Benefits:
Real-Time Trust Scores: AI can assign a trust score or fraud likelihood indicator by evaluating an address's historical behavior on the blockchain.
Proactive Intervention: With predictive analytics, Web3 platforms can identify potential rug pulls or fraudulent addresses before funds are lost—enabling preventive action rather than damage control.
Better User Experience: Users can verify counterparties through trust scores without revealing identities. Honest players gain credibility, and malicious actors are filtered out early.
6. Enabling Mainstream Adoption
Fraud is not just a nuisance; it’s a fundamental barrier to Web3’s mass adoption. Users must feel safe to transact, invest, and interact. The blueprint for success is evident, drawn from Web2’s experience:
Reduce Fraud with AI: Dynamically identify and neutralize fraudulent behavior in real-time.
Lower Acquisition Costs: Combine fraud detection with AI-driven intention-based marketing to effectively attract and retain genuine users.
With these solutions, Web3 can transform from a risky frontier into a trusted environment, encouraging more participation, higher liquidity, and sustainable growth.
Conclusion
The future of Web3 isn’t just about innovation in financial processes; it’s also about innovation in protection and trust-building. With AI at the helm, Web3 can leap the trust gap and fulfill its promise as the next big leap in the internet’s evolution.
AI-Based Predictive Fraud Detection in Web3: The Missing Key to Mainstream Adoption
Web3 is suffering from high fraud rates and low user trust. Traditional static, rules-based systems fail to prevent scams and rug pulls. What’s the solution? AI-driven dynamic fraud detection. Just as Web2 overcame early fraud issues with real-time, AI-driven transaction monitoring, Web3 must follow suit. By predicting fraudulent behavior before it happens, Web3 can create trust, attract users, and thrive.
1. Introduction
Welcome to XSpace #21, where Martin and Tarmo, co-founders of ChainAware, delve into the crucial topic of AI-based predictive fraud detection in Web3. In previous discussions, they have explored how AI-driven AdTech can reduce customer acquisition costs. Today, they focus on the other side—why robust fraud detection is essential for user trust and ecosystem growth.
2. The Lessons from Web2 Fraud Prevention
Web2 didn’t start as the seamless online shopping paradise we know. In its early days, fraud was rampant, credit card theft was common, and users hesitated to complete financial transactions online. This distrust slowed adoption.
How did Web2 overcome this hurdle? Financial institutions invested heavily in AI-based fraud detection. They used dynamic, continuously evolving algorithms to identify fraudulent patterns before transactions cleared. Over time, this approach lowered fraud rates and built user trust, making Web2 mainstream and reliable.
3. The Web3 Fraud Challenge
Today, Web3 faces a similar challenge. Despite offering innovative solutions like DeFi, self-custody, and permissionless finance, Web3 experiences high fraud rates:
Rug Pulls: Up to 95–98% of new PancakeSwap pools end in rug pulls. On some platforms, the rate is even higher. New users often lose everything in these scams, tarnishing the ecosystem’s reputation.
Impersonation and Social Engineering: Hackers and scammers frequently pose as service providers or experts, targeting unsuspecting users who don’t know who to trust.
High Hackers’ Fee: Around 2–3% of the total value locked (TVL) in DeFi gets stolen annually, a number that has not significantly decreased over time.
These problems mirror those of early Web2. Users who enter Web3 without proper protection are often burned by fraud and leave, taking their liquidity and trust with them.
4. Why Static Analysis Tools Aren’t Enough
Current Web3 anti-fraud measures often rely on static rules:
Crypto AML (Anti-Money Laundering): Tools track the flow of “bad” funds but are easily bypassed through mixers and complex routing.
Smart Contract Audits: While audits verify contract code at a given point, they cannot predict how a malicious actor will exploit dynamic, ever-evolving environments.
Because bad actors are innovative, static rules are quickly outdated. Just like early antivirus software in the PC era, which relied on matching known signatures and failed against polymorphic viruses, Web3 needs a more adaptive approach.
5. AI-Based Predictive Fraud Detection: A Game Changer
AI-driven fraud detection is dynamic, pattern-based, and continuously learning. Instead of checking a few static parameters, AI looks at historical behavior, transaction patterns, and evolving liquidity flows. It answers the critical question: What will this address or contract do next?
Learn more: Transaction Monitoring Agent
Key Benefits:
Real-Time Trust Scores: AI can assign a trust score or fraud likelihood indicator by evaluating an address’s historical behavior on the blockchain.
Proactive Intervention: With predictive analytics, Web3 platforms can identify potential rug pulls or fraudulent addresses before funds are lost—enabling preventive action rather than damage control.
Better User Experience: Users can verify counterparties through trust scores without revealing identities. Honest players gain credibility, and malicious actors are filtered out early.
6. Enabling Mainstream Adoption
Fraud is not just a nuisance; it’s a fundamental barrier to Web3’s mass adoption. Users must feel safe to transact, invest, and interact. The blueprint for success is evident, drawn from Web2’s experience:
Reduce Fraud with AI: Dynamically identify and neutralize fraudulent behavior in real-time.
Lower Acquisition Costs: Combine fraud detection with AI-driven intention-based marketing to effectively attract and retain genuine users.
With these solutions, Web3 can transform from a risky frontier into a trusted environment, encouraging more participation, higher liquidity, and sustainable growth.
Conclusion
The future of Web3 isn’t just about innovation in financial processes; it’s also about innovation in protection and trust-building. With AI at the helm, Web3 can leap the trust gap and fulfill its promise as the next big leap in the internet’s evolution.
