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Top1. Introduction
According to the American heritage dictionary, fraud is termed as the deception that is intentionally accomplished in order to perform the unlawful gain. However, fraud detection is used to identify the symptoms of fraud. Some of the examples that include fraud detection strategies are accounting fraud, credit card fraud, and insurance fraud. However, the information collected from Nigeria Inter-bank settlement system (NIBSS) showed that the fraudulent transactions are at peak levels in the banking sector. In recent decades, the frauds are being evolved and committed from casual fraudsters into the organized crime such that the fraud rings utilized some sophisticated techniques to control the commit and accounts of fraud. According to the research of Javelin in 2007, nearly 6.8 million Americans are offended through card fraud. In 2007, the frauds in the existing accounts faced $3 billion loss. Moreover, the Nilson report specifies the cost of the industry as $4.84 billion (Makki, et al., 2019; Nilson, 2007; John, et al., 2016). In 2007, Javelin states the transaction loss amount as % 30.6 billion (Kim, & Monathan, 2008; John, et al., 2016). In 2006, card fraud makes the transaction loss of 423 million according to the UK economy. Moreover, credit card fraud loses account transactions of $600 million globally each year (John, et al., 2016). Fraud is the criminal or wrongful deception that is intended to bring the personal or the financial gain (Zanetti, et al., 2017; John, et al., 2016; Randhawa, et al., 2018). The research works in fraud reduction are categorized into two topics (i) fraud detection and (ii) fraud prevention. Fraud prevention is the proactive approach, where the cause of fraud is stopped (Gowthami, et al., 2018; Cristin, et al., 2019). However, fraud detection is highly required when fraudulent transactions are accepted by the fraudster (Ruan, et al., 2019; Randhawa, et al., 2018).
In recent decades, financial fraud actions, like money laundering, credit card fraud are gradually increasing. However, these actions cause a loss in the enterprise or personal properties. The fraud activities will endanger the nation’s security, as the profit of fraud will move to terrorism (Huang, et al., 2018). Hence, tracing and detecting fraud accurately is urgent and necessary. Due to the complexity of transactions and trading networks, detecting financial fraud is a difficult task (Luo & Wan, 2019; Achituve, et al., 2019). By considering money laundering as an example, the money laundering is termed as the procedure to use the trades in order to shift the goods or money into funds. Mostly, the quality or the quantity of the goods, and their prices in the demand for money laundering will be probably fake. However, the misrepresentation of the quality or the number of goods and the prices in the invoice exposes some variations from the regular quality and the price basis. Under some conditions, these fraud detectors effectively work well under the entity of stable trading. Moreover, real-world situations are highly complicated under Free Trade Zones (FTZs), where international trades include the complex process in exchanging information among the trading entities. However, the fraud actions, like money laundering are the highest stealth. The money laundering actions include various forms (Sullivan & Smith, 2012; Huang, et al., 2018), like scaling and acquisition of intangibles, Medical equipment manufacturers (Barone, et al., 2002), concealing the transportation based on the trading functions, and the related party transactions. Different types of companies involve money laundering and trading goods. When compared with other fraud actions, money laundering shows special characteristics, as it is high risk in the financial system (Wang, et al., 2019; Yang, et al., 2019; Huang, et al., 2018).