Role of artificial intelligence in efficient crypto adoption
Artificial intelligence - the buzz around it
To this day, AI or artificial intelligence has certainly become a buzzword. Implementation of artificial intelligence by any business solely depends on their technology adoption curve. Increasingly businesses are incorporating AI into their businesses. For investment firms dealing with high volumes of investment products and investor’s wealth to manage, using AI and machine learning is inevitable.
Aarnâ protocol and autonomous alpha creation
Globally, crypto has become an $800 billion+ industry. Yet the majority of retail investors across the world have not really adopted crypto. However, the year of 2022 has seen some countries and major asset management firms like Blackrock think ahead of time and be able to recognize and amend their structure to incorporate digital assets or partner with crypto projects. Organisations in the digital assets space have become big enough to participate in world’s greatest events like the Qatar FIFA world cup. Besides, in light of recent events like the FTX collapse, a surge in Decentralized Finance or Defi has also been seen.
Nevertheless, the crypto industry demands efficiency and security to a greater extent in order to enable its mass adoption. Projects operating in this space are already far ahead in the technology curve. Integration of AI in crypto is not uncommon for the crypto industry these days. Here we discuss the use cases for AI in crypto and and its role in efficiently changing the perception of investors - from a gamble to a new asset class in the making. And in the process, how it can help in autonomous alpha creation.
How can AI solve barriers to entry in crypto
The major barrier to entry in crypto mass adoption is its complex nature in the understanding of the underlying technology. Besides, there are so many crypto projects - tokens listed on exchanges, operating on various chains with multiple functionalities, thus leading to information overloading and confusion. Recent past has seen major fraudulent activities leading to markets crashing and investors’ wealth being wiped out for good. Hence speculation and barriers in crypto continue to exist. Even if Defi has gained traction and there is a substantial rise in investments in Defi assets, it becomes difficult for retail investors to immediately adapt. Integration of AI into DeFi investment protocols can help overcome this in some ways.
Decluttering data, filtering the noise
Artificial Intelligence and Machine Learning play a significant role in augmenting the digital assets space, in the process, making it safer, more secure, and autonomous. Studying the market sentiments, analysing the users’ risk profile and recommending the top investment opportunities with higher returns can be substituted for by AI. Deep learning models, with data sources are able to generate automated insights, study alternative data sets and bring operational intelligence onto the table. Monitoring crypto markets and studying patterns to de risk fraudulent transactions or trading activities in crypto is something that the AI can do. It can also
Defi asset management protocols like aarnâ use models that run on blockchain meta data and gather social intelligence to process and provide insights to its users. Besides, predictive AI Modelling helps prevent losses, manoeuvre risks in crypto and get top recommendation for investments in crypto. It studies market sentiments and provides investor specific insights to help make right investment decisions. Using AI for Defi asset management significantly reduces risks and optimises returns.
Autonomous alpha creation is possible on an autonomous defi asset management protocol like aarnâ. AI, combined with human expertise and blockchain makes alpha available and accessible by all investors.
FAQs
Q: What is the alpha value chain?
A: The entire supply chain of alpha creation to distribution of alpha returns through alpha structured products, combined with intelligent access to crucial information and insights.
Q: What is predictive modelling?
A: Deep learning models run on historical data and observe patterns. The models are trained to process and learn and give predictive analysis.
Q: What is asset management?
A: Asset management is an age-old practice in the investment sector that gives an organised approach to invest in assets, create value and optimise returns for the future.
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