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Fall of Mantra (OM), the native token of the Layer-1 Real-World Asset Blockchain Mantra, rocked the Crypto Market on April 13th. Within hours, assets had plummeted from over $6 billion in market capitalization to around $500 million.
In a market that has already been damaged by the billion-dollar collapse, the collapse of the mantra’s native assets has once again proven that hacking is not the only enemy of the industry. Crypto is nullified by negligence. The team behind the mantra denounced the “forced liquidation” of 90% token crashes. This is only half the story.
As more data emerges, it is clear that collapse is not an unfortunate timing or high market volatility. It was a preventable disaster with many catalysts, including sophisticated locations, weak liquidity, and various gaps in automated risk management systems.
Ironically, artificial intelligence, a technology that Crypto Evangelists has praised for the past three years, could have predicted, flagged and even prevented this crash if properly implemented.
AI-driven fluidity stress test
The problem with traditional financial stress testing is that extreme volatility is designed for stable, regulated markets and traditional assets such as rare stocks and bonds. Cryptocurrencies, on the other hand, operate in another reality where wild prices change, sudden liquidity crashes are fairly common and are part of the market game. Legacy risk frameworks that rely on historical patterns cannot capture these shocks.
AI-driven stress testing offers a dynamic alternative. Instead of relying on static historical data, machine learning models adapt to real-time conditions and analyze market emotion, on-chain metrics, and liquidity patterns.
A new method called Kurtosis-based stress testing focuses on reducing the risk of “fat tail” events that characterize the failures of the crypto market, exactly the extreme outlier loss. This technique will help businesses crash with “unpredictable, unimpact” events like the recent mantra and 2022 Terra (LUNA). During the Terra collapse in 2022, traditional risk models failed as they didn’t expect how quickly Stablecoin de-Peg could spiral into a $60 billion wipeout.
The study shows that the portfolio, designed to reduce extreme risk swings, yields 491% returns on the kurtosis model, breaks a simpler “purchase and retention” approach at 426%, and is superior to those built around traditional Sharp ratio strategies with a return of 384%.
High kurtosis indicates a high probability of extreme volatility. In cryptography, these events are not an anomaly, they are part of the landscape.
Mantra’s weekend’s thin liquidity and exposure to token concentration could have been pre-flagged with AI-powered stress testing methods, providing stakeholders with a window into action before a catastrophe occurs.
Track and flag movements with AI
Blockchain transparency is its greatest strength, but it is impossible to manually monitor millions of transactions. This is where AI is great. Autonomous AI agents can continuously scan activity on the chain and flag unusual patterns that could indicate impending market manipulation without the need for human involvement.
In the case of Mantra, blockchain data analyzed after the crash revealed that it conveys signs. A few days before the collapse, a wallet linked to laser digital transferred 6.5 million OM tokens to another wallet, then sent to OKX, where it was reportedly liquidated. The AI surveillance system may have detected these movements in real time and issued immediate alerts to exchanges, regulators, and the broader community.
AI agents not only track transactions, but also build behavioral profiles across the wallet network, allowing them to distinguish everyday market behavior from potential operations.
Predicting order book vulnerabilities
Perhaps the most direct way AI has managed to prevent mantra conflicts is through sophisticated order book analysis. While purchase orders reveal the true health of the market, their complexity requires more than just surface-level analysis.
Deep learning models, particularly convolutional neural networks and long-term short-term memory networks, have been proven to produce promising results by predicting price movements based on order form data. One study found that time CNNS can predict price shifts in Bitcoin (BTC) with accuracy of up to 76%.
AI-driven analysis of market depth will highlight the risk of significant slippage from large sell orders. As a result, these models may have revealed mantra vulnerabilities by identifying dangerous thin orders during weekend trading hours.
With the help of AI and deep learning models, crypto companies can implement dynamic protection guards like circuit breakers triggered by sudden price drops and structural weaknesses of liquidity to flag or prevent mantra-like situations.
Use AI to build a resilient crypto ecosystem
Blockchain technology promises decentralization and transparency, but it remains vulnerable without an advanced AI-powered risk management system that can handle millions of transactions and flag suspicious patterns. The collapse of famous assets like Mantra and Terra proves the need for these systems.
Encrypted financial institutions should prioritize a dynamic stress testing framework that integrates both on-chain and off-chain data. Real-time transaction monitoring with AI agents must be a standard practice for exchange and liquidity providers. Continuous order book analysis is also important to predict slip risk and prevent operation-driven crashes.
At this point, crypto companies are struggling to keep up with global regulations, and every region has its own limitations. Sometimes it can take years for regulatory frameworks to be negotiated and properly evaluated. For example, the Crypto Asset Regulation (MICA) market was proposed in September 2020 and officially adopted on May 31, 2023, but was still incomplete. Several rules for stubcoin were announced in June 2024, and the Crypto Asset Service Provider regulations were announced in December 2024.
Despite the sensitivity of these regulations, they still cannot encapsulate the complexity, speed, and amount of data that defines the blockchain ecosystem. As a result, regulators remain with rules designed for yesterday’s issues.
Instead of imposing blankets and all-purpose restrictions to curb innovation, tools with more effective surveillance can also help regulators with more effective surveillance. Government agencies can focus on operational patterns and systematic risk detection without undermining decentralization principles in order to ultimately make timely and accurate decisions.
From prediction to prevention
Mantra crashes were inevitable. Most of the tools and techniques that can be predicted to exist already exist, but what is lacking is the will of the industry to implement them.
Rather than treating it as a separate domain, businesses need to integrate sophisticated and complex risk management integration into a broader enterprise framework. Investing in cross-working expertise across quantitative modeling, blockchain infrastructure and compliance is no longer a luxury. Market integrity must be protected.
Crypto companies should benchmark new global standards such as MICA and Basel’s cryptographic frameworks, leveraging both on-chain analysis and real-time exchange data for comprehensive monitoring.
Projects, exchanges, and institutions that embrace these methodologies will gain both competitive advantage and community trust. Most importantly, we can create a crypto ecosystem where innovation thrives without the constant threat of market manipulation or catastrophic crashes.
The problem is no longer when AI needs to be integrated into crypto risk management, but how quickly the industry is ready to accept it before the next crisis unfolds, hurting more investors. This is not only a protection for individuals, but also a reputation for the entire ecosystem.
All massive collapse, hacks and ragpur hurts the public’s trust in the crypto market. This allows regulators to push for stronger regulations.
AI can complement decentralized ecosystems, identify bad actors, detect systematic vulnerabilities, and detect those exploiting the system and individual trusted builders.
