Cryptocurrencies is considered a relatively recent domain that became active in the last decade. Bitcoin was announced at the end of 2008 as the first decentralized cryptocurrency that relies heavily on the field of cryptography for hashing and signing transactions. These transactions are committed to a distributed blockchain ledger to be synced and verified by nodes in a peer-to-peer network. The Bitcoin blockchain size reached over 280 GB in June, 2020.

With this big data representing transactions in the blockchain coupled with millions of trades being executed on different exchange websites, growing number of tweets, posts and articles related to Bitcoin and cryptocurrencies, there is a clear need for automated tools to process and analyze this big data. Artificial intelligence (AI) techniques can learn from this massive amount of data by analyzing and discovering patterns to ease and secure trading and mining. Discovering patterns in money-laundering transactions and other fraudulent transactions and trading schemes can help limit the crimes involving cryptocurrencies due to privacy and security threats they encounter. Artificial intelligence (AI) techniques are not limited to machine learning (ML) techniques (supervised, unsupervised, semi-supervised, and reinforcement), but also include evolutionary-based techniques and knowledge-based techniques.

AI research in Cryptocurrencies

Cryptocurrencies face similar challenges to fiat currencies’ and other financial market assets’ challenges. According to Business Insider Report2 in June 2019, there are three areas where AI techniques are used in banking, namely, conversational banking, anti-fraud detection, risk assessment and credit underwriting. 

Additionally, financial chatbots and voice assistants that mimic live employees, deepen customer relationships and provide personalized insights and recommendations, are examples of software systems using AI in the financial field. Moreover, AI is extensively used in intelligent trading systems to do stock market prediction and currency price prediction. This helps in taking decisions on when to buy, hold or sell a stock based on different markers that change over time. Furthermore, anti-fraud detection tasks make use of machine learning to learn from spending behaviors and patterns and detect suspicious patterns.

Key challenges

Challenges of Cryptocurrency can be tacked by AI techniques. Classification of the research work is done according to the challenge it addresses. Mentioned following are the key challenges:

• Price Prediction/Forecasting: The price of Bitcoin can be affected by many factors (sometimes called indicators/markers/features or variables), among them are the interaction between supply, demand and attractiveness for investors. These factors are usually affected by trends in social networks, forums, search engines, declarations by leaders and political stability of countries.
• Volatility Prediction: Volatility is defined as the degree of variation of a trading price series over time. It represents the amount of uncertainty or risk about the size of changes in currency value. Bitcoin and other cryptocurrencies are considered to be volatile. 
• Automated Trading:Many cryptocurrencies’ trading bots are currently available that implement trading strategies and offer customized customer’s strategy.
• Fraud Detection: The use and reputation of Bitcoin and other cryptocurrencies in aiding illicit activities is a big concern, as it affects the stability and the trust in cryptocurrencies. Cryptocurrencies are known to attract cybercriminals for their pseudo-anonymity and for being operated outside the laws of governments and banks.
• Anonymity and Privacy: Privacy and anonymity are two necessary aspects for online financial trading. Anonymity is mostly favored by criminals to hide their identities when dealing illegally for drugs or weapons or being involved in money laundering transactions.
• Cryptocurrency Mining: The mining process has the disadvantage of high electricity consumption used by mining pools for participating in the PoW computations. Only one miner succeeds to add a block of transactions, while other mining pools are left with the expenses of huge energy costs. This disadvantage threatens the decentralization of the cryptocurrency and makes it susceptible to monopolization.
• Security: Despite the security and privacy properties that exist in blockchain-based cryptocurrencies which were surveyed in, there are several security threats that are facing the cryptocurrency ecosystem. They can be classified as attacks on the distributed network, mining process attacks, double spending and transaction malleability attacks. 

Conclusion

Technology advances have impacted the cryptocurrencies evolution by creating new ways to mine new coins, store the blockchains over distributed nodes, secure the network and analyze the huge amount of trades and blockchain transactions that are beyond human capabilities.

The AI research studies addressing Bitcoin are remarkably more than those researching other altcoins. The possible dependencies between cryptocurrencies’ prices should be further identified. The possibility of using AI techniques to address security, anonymity and privacy level of other cryptocurrencies is recommended for further exploration as security and privacy are major and critical concerns for traders to gain more trust while trading.