This article talks about the advantages and disadvantages of using Machine Learning in the Crypto-Sphere. While Machine Learning can have positive impacts like Flow Analysis and Address Classification, it comes with disadvantages such as error occurrences. Read the entire article to find out what the final verdict says!
How are Machine Learning Applications transforming the Crypto-Sphere – The Pros and Cons!
Let’s Begin!
Deloitte Global predicts that the number of machine learning applications and implementations being used in the business industry will double by 2020! Another forecast by IDC says that spending on AI and Machine Learning will grow from $12B in 2017 to $57.6B by 2021.
Amazing it is! Isn’t it? With such a huge buzz around, it’s quite difficult to ignore a technology that is as impactful as Machine Learning (ML). It is an advanced application of AI that allows systems to perform tasks without being explicitly programmed. Machine Learning has NOW become a centerpiece of all strategies being used in the market space. This is why Greg Papadopoulo said this: “Machine Learning is going to result in a real revolution.”
It is “the craft of having computers make decisions without providing explicit instructions, thereby allowing the computers to pattern match complex situations and predict what will happen”, said Venkat Venkataramani, the co-founder and CEO of Rockset,
It is Machine Learning that people often talk about when they use the umbrella term “AI.” One can see a number of industries involved in leveraging ML benefits these days. Cryptosphere is one of these. So it’s worth taking the time to look at some of the incredible ML capabilities being deployed in the Crypto space. Here you go.
Recommended Article: Machine Learning Applications Across Different Industries
Pros of applying ML to the Crypto-Sphere!
Flow Analysis
With upcoming trends disrupting the market space every now and then, it is quite difficult at times to assess the value of an asset streaming in the business world. The best way to predict its future performance is by analyzing the flow of its funds.
Machine Learning can solve this complex problem. Let’s see how. By examining how funds are being transferred by known entities and then comparing this to previously known data sets, Machine Learning helps to predict value shifts in an asset.
Well, what does this mean, in the real world? Let’s explore to know more about this.
It means using Machine Learning applications, the traders can determine the strategic moves made by the big players in the cryptocurrency markets. For example, some cryptocurrency whales might be working to implement the pump and dump scheme, which would massively decrease the value of an asset.
Seeing this, an investor would decide to dump the asset. But, it may happen that instead of preparing a dump, the whales start accumulating the asset that had a downtrend. This will definitely put the investor to get on the ground floor before the asset significantly increased in value.
Using Machine Learning applications can help assess the value of these assets floating in the crypto space for allowing traders to do successful trading down the line. Besides the crypto market, ML has contributed a lot to the web development industry too. You can hire a web developer to integrate Machine Learning with your business projects.
Address Classification
Classifying wallet addresses is another essential task that can easily be performed using Machine Learning. ML acts as a powerful tool in classifying wallet addresses.
By identifying which wallet addresses are exchange wallets and individual wallets, Machine Learning models can predict the behavior of crypto exchanges by analyzing comprehensive data sets.
Earlier without using Machine Learning, it was impossible to classify wallet addresses due to a lack of relevant data. With ML, you can find the required data and explore whatever you desire. I guess, Tony Tether said this rightly, “Machine Learning is the next Internet”
Analyzing Trading Behaviors
Recurrent neural networks have become one of the popular ways of analyzing trading behaviors and determining the value of an asset in the cryptocurrency market.
Machine Learning allows users to predict the trading patterns of specific investors.
With the use of ML, the users can identify the investors in groups and help them in discovering the way in which the investors invest their capital.
To determine the performance of these assets, Machine Learning applications read the data and form an understanding of the asset through an analysis of how individuals interact with it since it is difficult to apply ML algorithms directly on an asset itself.
Fraud Detection
Another incredible way in which Machine Learning is being put to use in the Cryptosphere these days is for detecting fraudulent transactions. It’s been observed that the transactions made are nearly instantaneous.
Also, the information that gets recorded into the public ledger is not really identifiable data and any damage that is created by the fraudsters is permanent. The only way to mend this damage is by creating a fork in the blockchain which means effectively creating an entirely different cryptocurrency.
So, in order to detect fraud practices being applied to fiat currencies, major exchanges such as Coinbase had to implement some unique solutions. This is how Machine Learning came into the picture. Let’s see how it all happens.
Machine Learning applications are used for preventing these fraud practices. The machines are trained on well-known fraudulent patterns which helps the machines to get acquainted with the patterns so that they can identify when similar patterns are found.
By creating a safer, more secure marketplace for the investors within which they can trade their cryptocurrencies, Machine Learning plays a key role in the Crypto Sphere. Are you keen to avail Machine Learning benefits such as these in your business too? To do so, all that you need is to hire a Machine Learning Developer who can help you in leveraging Machine Learning to boost business profits.
Cons of applying ML to the Crypto-Sphere!
As rightly said, everything has its advantages and disadvantages and the same goes for Machine Learning too. It comes with its own pros and cons. While we already have looked at the pros of applying Machine Learning to Crypto-Sphere, let’s now take a quick glance at the cons also.
Time and Resources
Since analyzing data itself is a time-consuming task, that’s why ML algorithms take ample time to run and deliver results with accuracy. Moreover, the implementation of ML involves many resources to function which means more expenses.
Interpretation of Results
At times, using ML can be disadvantageous for you as ML algorithms may sometimes misinterpret the data leading to incorrect predictions. So, you must choose the right algorithm to get the right analysis of the specified data set.
High Error – Susceptibility
Machine Learning is highly susceptible to errors. Let’s take for example that if you train the algorithms with data sets that include limited cases. This may lead to the drawing of biased predictions coming from a biased training set.
Such mistakes if not detected at the right time can pose great problems. Later, when these problems get noticed, it may be difficult to recognize the source of the issue and even more difficulty can be faced while correcting these mistakes.
Machine Learning in Cryptosphere – The Final Verdict
As you have already seen, I have discussed both the good and the bad aspects of applying Machine Learning to the Crypto-Sphere.
While Machine Learning helps in assessing the value of an asset, it can sometimes lead to incorrect predictions by misinterpreting data. So, what you need to do take care of is to select the right ML algorithm that can analyze the data correctly without failure.
It is only then you will be able to leverage its potential for scaling your business growth just like the other industries existing in the business world are doing. The data says it all. Have a look at it!
Within the Business Intelligence (BI) & analytics market, Data Science platforms that support Machine Learning are predicted, by Forbes, to grow at a 13% CAGR through 2021.
Author Details
Ruchita Varma is a voracious reader and loves to write on technology-related articles. She holds a pretty good knowledge of the latest technology trends and is well-versed in creating content strategy deliverables across project life cycles. You can find her on LinkedIn. You can also visit her Website.
