Machine Learning and Deep Learning methods are widely adopted across financial domains to support trading activities, mobile banking, payments, and making customer credit decisions. These methods ...also play a vital role in combating financial crime, fraud, and cyberattacks. Financial crime is increasingly being committed over cyberspace, and cybercriminals are using a combination of hacking and social engineering techniques which are bypassing current financial and corporate institution security. With this comes a new umbrella term to capture the evolving landscape which is financial cybercrime. It is a combination of financial crime, hacking, and social engineering committed over cyberspace for the sole purpose of illegal economic gain. Identifying financial cybercrime-related activities is a hard problem, for example, a highly restrictive algorithm may block all suspicious activity obstructing genuine customer business. Navigating and identifying legitimate illicit transactions is not the only issue faced by financial institutions, there is a growing demand of transparency, fairness, and privacy from customers and regulators, which imposes unique constraints on the application of artificial intelligence methods to detect fraud-related activities. Traditionally, rule based systems and shallow anomaly detection methods have been applied to detect financial crime and fraud, but recent developments have seen graph based techniques and neural network models being used to tackle financial cybercrime. There is still a lack of a holistic understanding of the financial cybercrime ecosystem, relevant methods, and their drawbacks and new emerging open problems in this domain in spite of their popularity. In this survey, we aim to bridge the gap by studying the financial cybercrime ecosystem based on four axes: (a) different fraud methods adopted by criminals; (b) relevant systems, algorithms, drawbacks, constraints, and metrics used to combat each fraud type; (c) the relevant personas and stakeholders involved; (d) open and emerging problems in the financial cybercrime domain.
Forecasting the value of Ethereum (ETH) or any other cryptocurrency is a formidable undertaking owing to the inherent volatility and speculative characteristics shown by these digital assets. ...Nevertheless, it is possible to create price forecasts by using machine learning techniques, namely Recurrent Neural Networks (RNNs), which are capable of capturing temporal relationships within the data. The challenge of forecasting the price of Ethereum (ETH) or any cryptocurrency is a multifaceted endeavour that encompasses aspects of finance, economics, and data science. The practise of technical analysis is the examination of past price charts, patterns, and technical indicators in order to make forecasts about future price fluctuations. The underlying assumption is that previous pricing patterns had the capacity to provide valuable insights into future developments. Nevertheless, it is essential to acknowledge that the effectiveness of technical analysis within the realm of cryptocurrency trading is a subject that engenders much scholarly discourse. The primary objective of this research is to examine the utilisation of Ethereum cryptocurrency and forecast its behaviour via the use of machine learning methodologies, namely Recurrent Neural Networks. The suggested approach demonstrates a high level of accuracy, reaching 95 percent. This significant level of precision will be beneficial for future academics working on this technology.
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