Cryptocurrency is a type of digital money that is registered on decentralized, encrypted electronic ledgers and crypto analytics play an important role in our Technical life. The brave have better luck. Over the previous two years, Bitcoin has exploded into the public consciousness, leading many people to assume this. It’s hardly surprising that more and more Americans and institutions are embracing bitcoin at a faster rate when you consider that cryptocurrency companies have spent over $2 billion on advertising through Super Bowl advertisements, celebrity endorsements, and even arena acquisitions (thanks forthe memories, Staples Center).
Since August 2021, more than $2 billion has been invested in blockchain and cryptocurrency firms by Morgan Stanley, Goldman Sachs, and Citigroup. As a component of their 401k plans, Fidelity now includes Bitcoin. As part of its broader portfolio management strategy, Blackrock made it possible for Using Coinbase Prime, a prime broker, and institutional investors custody bitcoins using its Aladdin platform.
Businesses like Tesla, MicroStrategy, and Square have put their cash reserves straight into Bitcoin. Some nations are using cryptocurrencies as reserves in addition to gold. Whether we believe it will last or not, cryptocurrency is a part of our daily life. Although cryptocurrency has grown in popularity, its volatility has not. This article focuses on the dangers posed by the quickly expanding cryptocurrency sector and what resources and skills are required to safeguard businesses against potential crises in the future.
The Reasons for Using Cryptocurrency
Many new cryptocurrencies have entered the market since Bitcoin’s launch, and it continues to grow. According to data from Coinbase.com [3] as of May 15, 2021, there are 5,129 cryptocurrencies in circulation. According to Coinbase.com [3], Statista.com [4], and chainprint.tech [5], Bitcoin and Ethereum are the two most market-capitalized cryptocurrencies. Intriguingly, Elon Musk, CEO of Tesla, is promoting Dogecoin through his tweets [6]. Why someone would want to utilize cryptocurrencies, some of us may wonder.
A reliable and decentralised replacement for our current financial institutions was intended when cryptocurrency was first developed. Blockchain, a technique akin to bank balance sheets that continuously verifies transactions and makes all validated changes permanent [7], records and secures each and every cryptographic transaction. Instead of using human examination, the verifications are carried out using computation (solving algorithms). Both banks and governments are not acting as mediators. Instead, crypto blockchains are dispersed and shared among all network participants, creating a decentralised system that allows all players to fully own their assets. Let’s examine big data analytics’ potential for this now.
Cryptocurrency Forecasting Using AI
Here are some methods for applying AI cryptocurrencies to predict bitcoin price and market volatility:
- The GARCH model was used by Peng et al. to combine support vector regression with the task of figuring out the volatility of cryptocurrencies. Both low volatility frequencies and high volatility frequencies were correctly predicted by the implementation [9].
- In order to forecast Bitcoin price, Jang and Lee used Bayesian neural networks on blockchain data. Low mistake rates were a sign of successful deployment [10].
- For the purpose of forecasting the trend in the price of bitcoin, McNally et al. used Bayesian long short-term memory networks [11].
- In order to forecast Bitcoin intraday trade, Nakano et al. used neural networks [12].
Big data analytics can be used to forecast market volatility [13] and trustworthy implementations can be further developed as cryptocurrency trading systems. Similar strategies can be used in cryptocurrencies, as Wall Street executes roughly 90% of its trade activity algorithmically [14]. However, these data-driven, automated cryptocurrency trading systems have not yet reached the proficiency of professional humans [15]. Additionally, crypto analytics are now quite significant.
Using Big Data Analytics, blockchain security can be improved
Peer-to-peer data exchange is made safe and irreversible by blockchain technology in a decentralised crypto network. However, a system with no central authority is prone to cybercrime, thus it needs to be constantly guarded against. Artificial intelligence (AI)-powered cryptocurrencies can be used to improve the security of crypto networks by spotting suspicious user activity, thwarting thefts, and preventing data leaks [15]. Cryptocurrency may continue to be a safe location to conduct transactions by proactively identifying suspicious patterns and behaviors in the blockchain.
Risk management with data science
Let’s now examine how AI might be used to monitor and control crypto hazards after discussing data science and crypto for market price prediction and blockchain security enhancement. External factors that affect cryptocurrency volatility include public opinion, blockchain forking, regulatory changes, the emergence of rivals, technological developments, significant societal events, and marketing initiatives. It is feasible to forecast potential changes in cryptocurrencies by fusing data from various sources with transaction data for analysis.
Forking and halving are two occasions that typically cause the price of Bitcoin to rise dramatically. Prior to 2012, every successful Bitcoin block validation resulted in blockchain miners receiving 50 Bitcoins [21]. The rewards per validated block were reduced in May 2021, to 6.25 Bitcoins. When a blockchain for a cryptocurrency forks, two independent transactional records are created. Naturally, these have an impact on the value and market capitalization of any involved coin.
Making automated investment judgments is possible thanks to the enormous amount of data created by cryptocurrency transactions. We cannot discuss automation without bringing up data science, big data, and artificial intelligence (AI). Automated decision-making is a key component of AI, which focuses on utilizing algorithms to learn from data. Machine learning is used in the interdisciplinary field of data science to produce reports, visualisations, and insights from data that may be used to make decisions. The five V’s of mass data—volume, velocity, variety, veracity, and value—are the focus of the big data research sector, which aims to develop suitable hardware and software infrastructure.
Smart Fraud Detection System
Fraud attempts are a constant threat to the cryptocurrency business, which is expanding quickly. The first four months of 2021 alone had a loss of 432 million USD, up from 4.5 billion USD for 2019 and 1.9 billion USD for 2020, according to Cipher Trace [29], a crypto hacking company. Scams involving initial coin offerings (ICOs) target consumers who are not familiar with the cryptocurrency industry. More than 80% of ICOs in 2017 were identified as scams, according to Statis Group, an ICO company [30].
Using cryptocurrency data analysis, the following methods for spotting fraud and scams have been tried:
- Bian et al. used text mining to find ICO fraud projects using 2,251 crypto evaluations. 83% accuracy was obtained from the implementation [31].
- Machine learning was used by Xu and Livshits to find pump and dump indications in cryptocurrency [32].
- To identify indications of Ponzi schemes on the Bitcoin network, Bartoletti et al. employed data classification [33].
- The Ethereum network was used by Chen et al. to identify Ponzi scheme indicators [34].
The three topics of analyses and forecasts, enhancing blockchain security, and risk management are presented as applications of data science and big data analytics in the cryptocurrency space.
In comparison to AI, the cryptosphere is a recent subject. Although there are many ways in which data-driven methodologies might boost bitcoin operations, it appears that, at least for the time being, public sentiment is the main factor influencing crypto value variations. We are interested in the future developments of this technology.
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