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The fall of Mantra (OM), the native token of the layer-1 real-world asset blockchain Mantra, shook the crypto market on April 13. Within hours, the asset saw its market cap plunge from over $6 billion to around $500 million.
In a market already scarred by billion-dollar collapses, the collapse of Mantra’s native asset proved yet again that hacks aren’t the only enemy to the industry—crypto has been crippled by negligence. The team behind Mantra blamed “forced liquidations” for the 90% token crash, which is only half of the story.
As more data surfaces, it’s becoming clear that the collapse wasn’t just a case of unfortunate timing or high market volatility. It was a preventable disaster that had many catalysts, like overleveraged positions, weak liquidity, and various gaps in its automated risk management systems.
Ironically, artificial intelligence, the technology that crypto evangelists have been praising over the last three years, could have predicted, flagged, and even prevented this crash, had it been implemented properly.
AI-driven liquidity stress testing
The problem with traditional financial stress testing is that it is designed for stable, regulated markets and conventional assets like stocks and bonds, where extreme volatility is rare. Cryptocurrencies, on the other hand, operate in a different reality where wild price swings and sudden liquidity crashes are pretty common and part of the market game. Legacy risk frameworks that rely on historical patterns fail to 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, analyzing market sentiment, on-chain metrics, and liquidity patterns.
A new method called kurtosis-based stress testing focuses on reducing the risk of extreme outlier losses, precisely the “fat tail” events that characterize crypto market failures. This technique can help firms in “less predictable, high-impact” events like the recent Mantra and the 2022 Terra (LUNA) crashes. During the Terra collapse in 2022, traditional risk models failed because they didn’t anticipate how quickly a stablecoin de-peg could spiral into a $60 billion wipeout.
The research shows that portfolios designed to reduce extreme risk swings delivered a 491% return with the kurtosis model, beating the simpler ‘buy-and-hold’ approach at 426% and even outperforming those built around traditional Sharpe ratio strategies, with a 384% return.
A high kurtosis value indicates a higher probability of extreme volatility. In crypto, these events aren’t anomalies—they’re part of the landscape.
Mantra’s exposure to thin weekend liquidity and token concentration could have been flagged well in advance with AI-powered stress testing methods, offering stakeholders a window to act before catastrophe struck.
Tracking and flagging movements with AI
Blockchain’s transparency is its greatest strength, yet monitoring millions of transactions manually is impossible. This is where AI excels. Autonomous AI agents can continuously scan on-chain activity and flag unusual patterns that might indicate impending market manipulation, all without the need for human involvement.
In Mantra’s case, blockchain data analyzed after the crash revealed telling signs. Just days before the collapse, a wallet linked to Laser Digital reportedly transferred 6.5 million OM tokens to another wallet, which then sent them to OKX, where they were liquidated. An AI monitoring system could have detected these movements in real time, issuing immediate alerts to exchanges, regulators, and the broader community.
AI agents can distinguish routine market behavior from potential manipulations since they don’t just track transactions but also build behavioral profiles across wallet networks.
Predicting order book vulnerabilities
Perhaps the most direct way AI could have prevented the Mantra crash is through sophisticated order book analysis. Order books reveal the true health of a market, but their complexity demands more than just surface-level analysis.
Deep learning models, particularly Convolutional Neural Networks and Long Short-Term Memory networks, have proven to deliver promising results in forecasting price movements based on order book data. One study found that temporal CNNs can predict Bitcoin (BTC) price shifts with up to 76% accuracy.
AI-driven analysis of market depth would have highlighted the risk of significant slippage from large sell orders—conditions ripe for a cascading price collapse. Consequently, these models could have exposed Mantra’s fragility by identifying dangerously thin order books during weekend trading hours.
With the help of AI and deep learning models, crypto firms can implement dynamic safeguards like circuit breakers triggered by sharp price drops and structural weaknesses in liquidity to flag or prevent situations similar to Mantra.
Building a resilient crypto ecosystem with AI
While blockchain technology promises decentralization and transparency, it remains vulnerable without advanced AI-powered risk management systems that can process millions of transactions and flag suspicious patterns. The collapse of high-profile assets like Mantra and Terra has proven the need for these systems.
Financial institutions with crypto exposure must prioritize dynamic stress testing frameworks that integrate both on-chain and off-chain data. Real-time transaction monitoring, powered by AI agents, needs to be the standard practice for exchanges and liquidity providers. Continuous order book analysis is also crucial to anticipate slippage risks and prevent manipulation-driven crashes.
At this point, crypto companies are having a hard time catching up to the global regulations, with every region having its own set of limitations. Sometimes, regulatory frameworks take years to be negotiated and assessed properly. The Markets in Crypto-Assets Regulation (MiCA), for example, was proposed in September 2020 and was officially adopted on May 31, 2023, but was still incomplete—some rules for stablecoins were announced in June 2024, and provisions for crypto-asset service providers were announced in December 2024.
Despite the sensitivity of these regulations, they still fail to encapsulate the complexity, speed, and volume of data that define blockchain ecosystems today. Consequently, regulators are left with rules designed for yesterday’s problems.
Instead of imposing blanket and one-size-fits-all restrictions that stifle innovation, AI-powered tools can also help regulatory bodies with more effective oversight. Government agencies can focus on detecting manipulation patterns and systemic risks without compromising decentralization principles to ultimately make timely and accurate decisions.
From prediction to prevention
The Mantra crash wasn’t inevitable. Most of the tools and techniques that could’ve predicted it already exist, but what’s missing is the industry’s will to implement them.
Companies need to start integrating advanced and more complex risk management into broader enterprise frameworks rather than treating it as a separate domain. Investing in cross-functional expertise spanning quantitative modeling, blockchain infrastructure, and compliance isn’t just a luxury anymore; it’s a necessity to protect market integrity.
Crypto firms should benchmark against emerging global standards like MiCA and Basel crypto frameworks and leverage both on-chain analytics and real-time exchange data for comprehensive monitoring.
The projects, exchanges, and institutions that embrace these methodologies will gain both competitive advantage and community trust. Most importantly, they can build a crypto ecosystem where innovation thrives without the constant threat of market manipulation and catastrophic crashes.
The question is no longer if AI should be integrated into crypto risk management, but how soon the industry is ready to embrace it before the next crisis unfolds, and more investors are hurt. This isn’t just about protecting individuals, but also the reputation of the whole ecosystem.
Every major collapse, hack, and rug pull hurts the public’s trust in the crypto market. This allows the regulators to push for stronger regulations.
AI can complement the decentralized ecosystem and help identify bad actors, detect systemic vulnerabilities, and separate credible builders from those exploiting the system.
