AI Could Have Stopped Mantra’s $OM Crash—and Can Prevent the Next One

Key Takeaways

• 
  Forced liquidations likely triggered Mantra’s 90% drop during low liquidity.
  
• 
  AI could have detected risky wallet movements in real time, alerting risk teams to act before the collapse.
  
• 
  However, artificial intelligence may also help prevent other kinds of fraud.
  

In the high-speed sector of crypto markets, milliseconds can mean millions. Yet most exchanges still rely on outdated systems, blind to the early warning signs hidden in wallet movements, order book depth, and social sentiment.

What if a device had been able to identify the
cracks
forming before the collapse?

As artificial intelligence becomes more prevalent in trading systems, experts suggest that the technologies to avoid crashes like this are becoming increasingly important.
Mantra’s
Already present but not being utilized.

The Role of AI in Avoiding Accidents as a Mantra

On April 13, Mantra (OM), a cryptocurrency initiative focused on real-world assets (RWAs),
dropped by 90% within hours
, plummeting from $6 down to under $1. Early concerns about a sudden sell-off were ruled out in favor of a more likely explanation: mandatory sales caused by highly leveraged futures trades when market liquidity was low.

Ahmad Shadid, the founder and CEO of O.XYZ, believes that employing an AI agent might have stopped the crash and promptly identified and resolved the problem.

Initially, AI enables us to conduct continuous living stress tests rather than relying solely on quarterly spreadsheet exercises that exchanges currently use. Neural networks integrate real-time order book depth with on-chain data.
wallet
Flows, along with even Telegram sentiment to simulate ‘fat-tail’ scenarios,” explained Shadid to .

“Secondly, deep-learning models trained on historical book data have the capability to predict spikes in slippage within milliseconds,” according to Shadid.

For the creator of O.XYZ, studies on temporal Convolutional Neural Networks have achieved a 76% walk-forward accuracy in forecasting subsequent price movements using limit-order book images, demonstrating that machines possess this capability.

see

The vulnerability that people overlook.

In
Mantra’s case
, wallets connected to strategic investors transferred millions of OM tokens to OKX days before the price drop.

“AIs would have warned the risk departments instantly, giving everybody the crucial minutes or hours required to broaden spreads, provide additional collateral, or halt trading until liquidity was restored,” according to Shadid.

Exchanges Move Gradually Towards AI Surveillance Technologies

Centralized exchanges (CEXs)
AI is needed the most as opaque trading orders and liquidity engines increase risks. Incorporating AI into current data streams (such as those provided by
Kaiko
) provides immediate insight into slippage without disclosing user information.

DeFi data is public and on-chain, allowing
AI
to monitor multiple chains simultaneously. While execution is slower due to on-chain settlement, the same AI tools can be adapted to different infrastructure.

Among the key adoption challenges, Shadid highlighted three in particular:

• 
  Data Access: CEXs tightly guard detailed order data, making it hard to train accurate models. Adoption hinges on showing that shared risk tools reduce reputational damage.
  
• 
  Regulatory Requirements: Authorities call for openness. Opaque artificial intelligence will not suffice—the models need to provide clear explanations for their alerts.
  
• 
  Calculate Expenses: Real-time AI requires substantial GPU resources. Distributed GPU networks provide cost-effective, on-demand computing, which makes it feasible for more modest platforms.
  

He mentioned that the most challenging aspect is data governance.

Liquidation logs and comprehensive order books at Legacy CEXs are treated as precious assets; lacking this detailed information, the algorithms fail to perform accurately and generate numerous incorrect alerts.

Persuading them involves demonstrating that a common risk tier diminishes brand damage expenses more effectively than it discloses proprietary information.


”


Shadid added.

Emerging Tech Could Predict Insider Trading Activities

However, AI involvement doesn’t stop at crashes such as Mantra’s; it can also assist in identifying and thwarting insider trading.

AI groups wallets based on their activity patterns and highlights abnormal transactions, like significant accounts transferring tokens.
CEXs
During periods of low liquidity, these patterns emerged prior to the Terra and Mantra crashes, providing risk teams with an opportunity to take action.

For Shadid, pattern recognition tools group wallets based on behavioral fingerprints (such as holding periods, exchange bridges, and collateral for loans), and they highlight irregularities like several major holders transferring tokens to the same centralized exchange during times of low liquidity.

“Those patterns preceded both
Terra
And with Mantra; apprehending them just a few minutes sooner can be sufficient for risk desks to increase margin requirements or lock down suspicious accounts.


”


he told .

We combine this with indicators for sentiment and depth: when large-scale deposits align with expanding spreads and unfavorable social sentiment, the likelihood of a concerted sell-off increases significantly.

As Shadid explains, the system measures that convergence and triggers tiered alerts – starting with “keep a close watch”


”


to “auto-halt market-orders


”


.

“During backtests using historical LOB and on-chain information, the model identifies dubious activity several hours prior to significant drops in prices, showing twice the accuracy compared to human monitoring through manual methods. This isn’t about predicting the future; rather, it’s about allowing data to communicate more swiftly than people could sift through blockchain explorers,” Shadid stated.

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