“Risk comes from not knowing what you’re doing,” as Warren Buffett famously said. That’s also to say uncertainty—i.e., not knowing what’s next—produces risk, because we can’t plan for uncertainty. This applies particularly to nascent crypto markets. More so, given recent macroeconomic and geopolitical developments.
Per IMF’s latest World Uncertainty Index, “from Brexit and US-China trade tensions to the pandemic and war, successive shocks have combined to keep uncertainty elevated.” Moreover, since global debt rose by over $12 trillion during the pandemic and inflation remains much higher than central bank targets in 2023, financial authorities have largely struggled to achieve stability worldwide.
As a combined effect of these developments, we witnessed the recent banking crisis in the US, which isn’t over still, according to Jamie Dimon, CEO at JP Morgan Chase & Co. The meltdown also played a key role in bringing the macro pressure to crypto markets more directly as crypto-friendly entities like Silicon Valley Bank and Signature Bank went under.
But this can’t go on for long. Otherwise, we might go from uncertainty to a full-blown global economic crisis, comparable in scale to the 2008 Financial Crisis. While some analysts say the banking fiasco, for one, can foster greater decentralization, robust frameworks to eliminate systemic uncertainty are imperative. And thanks to AI’s speedy evolution, we can now build new-age predictive analysis tools for enhanced risk management and mitigation.
From speculation to knowledge
Speculation has been a key facet in crypto markets, especially during bull runs. High-risk traders have made big bucks, even as naysayers pretty much turned “speculation” into a cuss word. But while it’s naive to cancel speculation altogether—it has considerable merits from a disruption PoV—it’s also not the ultimate way forward for crypto.
Besides ‘solving real-world problems’, it’s also important to build tools and financial instruments keeping the average user in mind. They don’t want to speculate. For most parts, they want certainty, thanks to the conditioning of legacy instruments like ‘fixed-income’ assets. That’s why non-speculative, data-driven tools are crucial to meet crypto’s mass adoption goals.
Even for those who wish to play the high-risk-high-gain game, deeper insights into the market trajectory and so on are a boon. And particularly as the industry matures, there’ll be a greater demand for knowledge-driven actions—i.e., well-informed decisions—rather than guesswork. Because, as discussed above, risk due to uncertainty is a bad risk, not profitable for anyone.
Over time, users will increasingly want to know what they’re getting into. At least that’s what stakeholders of the crypto industry must push for, since it’s a key differentiator vis-à-vis legacy systems. Transparency and autonomy are, after all, the lynchpin of crypto’s world-changing vision. We need systems that make users knowledgeable, mainly through access to information.
Leveraging data for stabler markets
For end-users to access rich analytics or such information for better decision-making, the system itself must have the processes to leverage data effectively. That’s where AI-powered predictive analytics tools can do wonders, empowering innovators to develop and implement data-driven processes.
Coupled with other emerging technologies like blockchain and cryptography, such new-age systems can have certainty in-built because the “knowledge” is codified and automated. Meaning, that even when users are somehow unable to gather 100% accurate knowledge about the underlying framework, they get the benefits of smart, far-sighted, and holistic execution.
For example, predictive analytics can help identify market patterns by analyzing historical trading data, order book information, market depth, etc. This in turn can be used to gauge, say, the liquidity levels across exchanges, helping users (or their algorithms) anticipate liquidity fluctuations and make decisions accordingly.
Robust AI models can similarly be used to analyze and predict price movements, enabling competitive pricing and enhancing profitability. More importantly, risk assessment gets a significant boost with smart analytics models that can identify negative patterns from such vast data sets that individuals can’t ever analyze or access.
Overall, when implemented with automated execution engines and smart order routers, predictive models can effectively distribute liquidity access and bolster certainty across market scenarios. They’re important for making crypto markets stabler and more resilient from within, giving the community means to weather external pressure.
Making sense of the available data is the most viable means to knowing what we’re getting into and what lies ahead. And with predictive analytics, technology meets knowledge, so to say, while code ensures that users benefit from it, no matter what. Of course, crypto’s progressive, user-oriented principles play a key role here: decentralization, transparency, trustlessness, etc.
Last but not least, AI-powered predictive analytics gives yet another example of how humans can utilize AI for good, rather than the latter taking us over in some apocalyptic setting. If at all, AI-powered tools can help us avoid an apocalyptive situation where our financial futures are mired in uncertainty and undue risks.