Ai Can Help Crypto Avoid Severe Liquidity Crises As ‘Minsky Moment’ Threat Grows

@ahmedismail

Ahmed Ismail is the Co-Founder and CEO of FluidAI.

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Banks have toppled like dominoes recently while financial authorities struggle to balance inflation control and financial stability. Experts at JPMorgan and elsewhere think a ‘Minsky Moment’ is likely, bringing back the horror of 2008. But despite the popular narrative, it’s not all doom and gloom. There’s much scope for redemption, especially in emerging industries like crypto.

From Credit Suisse to Silvergate, Signature, and Silicon Valley Bank (SVB), the fallen institutions mostly succumbed to a deep and systemic liquidity crisis. For instance, SVB’s Treasury Bond holdings lost their market value as the U.S. Federal Reserve raised interest rates to curb inflation.

Panicked depositors made a run on SVB, forcing it to sell long-term bonds at a massive $1.8 billion loss to generate liquidity for withdrawals. Stock prices also fell sharply due to rampant sell-offs. Revenue streams dried up further, worsening the liquidity crunch—the bank was stuck in a vicious cycle.

Crypto-focused Silvergate Bank faced a similar liquidity crisis, ultimately calling it quits. This was due to some of its top clients, including Coinbase, moving their funds out. Like SVB, Silvergate had to sell assets at a loss to honor roughly $8 billion worth of consumer withdrawals.

This shows how emerging crypto markets are as much prone to systemic liquidity crises as their legacy counterparts. Particularly because it has to deal with internal black swan events like the LUNA and FTX crashes, besides the macroeconomic ripples. And it points to the pressing need for long-term solutions, which, as we shall soon see, Artificial Intelligence (AI) can enable.

Crypto’s fragmented liquidity is a shot in the foot.

The crypto industry isn’t facing a full-blown liquidity crisis yet. But the highly fragmented nature of the industry’s liquidity landscape doesn’t help its chances of avoiding such crises. More so in such uncertain times when increasing borrowing costs dries up credit lines and revenue streams.

The total market cap of cryptocurrencies is over $1 trillion as of March 2023. Though 3x lower than its previous peak in November 2021, this represents the immense value already locked in crypto. However, these assets are highly concentrated in a few exchanges—i.e., the top five-ten CEXs and DEXs. That is to say, crypto’s liquidity is held in silos with low inter-protocol utility.

Centralized liquidity models have several issues, like high slippage risks and pricing discrepancies. But most importantly, they make it difficult for consumers to exit their positions during crises. The FTX fiasco was a glaring example of how investor funds remain stuck in faltering platforms. And the slower someone can respond in such scenarios, the greater their loss.

The other problematic aspect of fragmented liquidity is underutilization. Crypto markets can never tap the total liquidity available as long as there are silos. Yet, imagine how much more effective these markets will be when it’s possible to route liquidity on-demand across ecosystems.

Operational inefficiencies aside, crypto-based institutions are far less likely to face liquidity challenges if the industry properly utilizes the available resources. It’ll also stabilize crypto markets. The institutional investors who remained on the sideline due to the uncertainty since 2022 can also regain their confidence.

AI provides the tools to tackle crypto’s black swans.

AI can help solve both the liquidity-related problems discussed above: underutilization and the lack of exit points. That’s how innovative platforms ensure the best execution of cryptocurrency trades, minimizing slippage and improving price discovery.

Crypto’s market dynamics are heavily driven by social sentiments. Elon Musk’s tweets can take Doge to the moon; Changpeng Zhao’s comment triggered the FTT sell-offs, ultimately bringing FTX down. And this is one reason why traditional Quant-based prediction models don't work well with crypto markets.

Some systems analyze large chunks of unstructured data—social media posts, impressions, etc.—using various strands of AI, including deep and machine learning. This allows the tool to predict crypto markets with high accuracy and enables effective trading decisions.

Moreover, combined with liquidity aggregator protocols, these AI-powered systems scan the entire crypto landscape to find the best trading opportunities. One, this allows proper utilization of the liquidity available in every part of the broad crypto ecosystem. Two, it improves the chances of exiting a position promptly to avoid major losses.

Both of the above capabilities are necessary to tackle the ripple effects of black swan events. They are the keys to stopping a potential ‘Minsky Moment’ from turning into a full-blown liquidity crisis. AI-based solutions cannot predict or stop black swan events. But accurate market predictions empower investors to react, respond, and repair their positions in time.

Restoring trust and confidence is the way forward.

The past year has taught the crypto industry many lessons. The LUNA crash highlighted the need to build resilience into crypto-based financial systems. FTX’s collapse showed how operational inefficiencies and gross liquidity mismanagement can even drive giants to their doom. And finally, the ongoing banking crisis provides a reference to where things can go unless the crypto industry mends its ways.

There’s no practical way for industry stakeholders to control external factors like interest rate hikes. But it’s totally possible to innovate solutions for optimal liquidity management and utilization. That way, crypto can make the most of the value it already has, even as capital influx remains low.

Restoring trust and confidence is absolutely essential for the crypto ecosystem to come out of this muddle. This, again, reinforces the need for smart liquidity solutions because that’s where the problem began in the first place.

So, instead of fanning naive AI vs. Crypto debates, it’s best to explore how these emerging technologies can together provide the foundation for robust liquidity infrastructures. These will ultimately form the basis for stable and mature crypto markets, fostering long-term adoption by retail and institutional players.

Last but not least, solving the liquidity crisis will ensure better consumer protection even when the markets bleed due to external factors. This is perhaps the best and most necessary outcome—the true mark of a progressive, user-centric domain like crypto.