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 Go to Source to See Full Article
Author: Guest Post