“…The authors applied an RNN-EL model for stock price manipulation problems using data sets with trading data and characteristic company features. Islam et al [35] proposed detecting illegal insider trading of stocks by using proactive data mining on illegal insider trading cases and historical stock volume data. Furthermore, this research was the first such study of illegal insider trading using real cases.…”
Section: B Anomaly Detection Using Deep Learningmentioning
Instability in financial markets represents a considerable risk to investors; examples of instability include a market crash caused by systematic risks and abnormal stock price volatility caused by artificial hype. The early detection of abnormal behavior can help investors adjust their strategy and reduce investment risks. We proposed a spatiotemporal convolutional neural network-based relational network (STCNN-RN) model that can learn the complex correlations between multiple financial timeseries data sets, and we used genetic algorithms with a constrained gene to discover the time points for outlier companies by fitting the STCNN-RN model; we used these outlier points to identify abnormal situations. Most research on identifying anomalous patterns has been unable to sufficiently explain the reason for anomalies to investors. We applied an interpretability model to enable investors to understand these anomalous time points in relation to companies and discover the key factors giving rise to the anomalies. The experiment results revealed that the proposed model can be used to model multiple financial time-series data sets and to capture anomalous situations in relevant companies. Because this study explored the discovery of anomaly phenomena in all transaction data and the explanation of these abnormalities, investors can understand a stock market situation holistically.
“…The authors applied an RNN-EL model for stock price manipulation problems using data sets with trading data and characteristic company features. Islam et al [35] proposed detecting illegal insider trading of stocks by using proactive data mining on illegal insider trading cases and historical stock volume data. Furthermore, this research was the first such study of illegal insider trading using real cases.…”
Section: B Anomaly Detection Using Deep Learningmentioning
Instability in financial markets represents a considerable risk to investors; examples of instability include a market crash caused by systematic risks and abnormal stock price volatility caused by artificial hype. The early detection of abnormal behavior can help investors adjust their strategy and reduce investment risks. We proposed a spatiotemporal convolutional neural network-based relational network (STCNN-RN) model that can learn the complex correlations between multiple financial timeseries data sets, and we used genetic algorithms with a constrained gene to discover the time points for outlier companies by fitting the STCNN-RN model; we used these outlier points to identify abnormal situations. Most research on identifying anomalous patterns has been unable to sufficiently explain the reason for anomalies to investors. We applied an interpretability model to enable investors to understand these anomalous time points in relation to companies and discover the key factors giving rise to the anomalies. The experiment results revealed that the proposed model can be used to model multiple financial time-series data sets and to capture anomalous situations in relevant companies. Because this study explored the discovery of anomaly phenomena in all transaction data and the explanation of these abnormalities, investors can understand a stock market situation holistically.
“…Extremely Randomized Trees or Extra Trees (ET) is also a tree-based ensemble technique like RF and shares a similar concept with Random Forest (RF). The only differences are in the process of selecting the splitting attribute and in determining the threshold (cutoff) value; both are chosen in extremely random fashion (Islam et al, 2018b). As in RF, a random subset of features is taken into consideration for the split selection but instead of choosing the most discriminative cut off threshold, here in ET, initially the cut off thresholds are set to random values.…”
In the area of credit risk analytics, current Bankruptcy Prediction Models (BPMs) struggle with (a) the availability of comprehensive and real-world data sets and (b) the presence of extreme class imbalance in the data (i.e., very few samples for the minority class) that degrades the performance of the prediction model. Moreover, little research has compared the relative performance of well-known BPM's on public datasets addressing the class imbalance problem. In this work, we apply eight classes of wellknown BPMs, as suggested by a review of decades of literature, on a new public dataset named Freddie Mac Single-Family Loan-Level Dataset with resampling (i.e., adding synthetic minority samples) of the minority class to tackle class imbalance. Additionally, we apply some recent AI techniques (e.g., tree-based ensemble techniques) that demonstrate potentially better results on models trained with resampled data. In addition, from the analysis of 19 years (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) of data, we discover that models behave differently when presented with sudden changes in the economy (e.g., a global financial crisis) resulting in abrupt fluctuations in the national default rate. In summary, this study should aid practitioners/researchers in determining the appropriate model with respect to data that contains a class imbalance and various economic stages.
“…Financial fraud detection at the customer level is mainly related to individual financial activities, including health care insurance, automobile insurance, credit card, loans, e-commerce transaction, and so on, 8 , 12 whereas business-level fraud crimes, such as financial statement misconduct and money laundering, are often committed by syndicates accompanied by other crimes such as bribery, tax evasion, and even support of terrorism. 13 , 14 , 15 Figure 1 The classification of financial fraud types …”
Section: Introductionmentioning
confidence: 99%
“…Financial fraud detection at the customer level is mainly related to individual financial activities, including health care insurance, automobile insurance, credit card, loans, e-commerce transaction, and so on, 8,12 whereas business-level fraud crimes, such as financial statement misconduct and money laundering, are often committed by syndicates accompanied by other crimes such as bribery, tax evasion, and even support of terrorism. [13][14][15] The ongoing COVID-19 pandemic brings unexpected sudden shock to the global financial system and accelerates the use of digital financial services. 16 These changes have escalated more insidious fraud schemes, providing a breeding ground for all types of financial fraud.…”
The great losses caused by financial fraud have attracted continuous attention from academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus pandemic (COVID-19) unexpectedly shocks the global financial system and accelerates the use of digital financial services, which brings new challenges in effective financial fraud detection. This paper provides a comprehensive overview of intelligent financial fraud detection practices. We analyze the new features of fraud risk caused by the pandemic and review the development of data types used in fraud detection practices from quantitative tabular data to various unstructured data. The evolution of methods in financial fraud detection is summarized, and the emerging Graph Neural Network methods in the post-pandemic era are discussed in particular. Finally, some of the key challenges and potential directions are proposed to provide inspiring information on intelligent financial fraud detection in the future.
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