Identifying Money Laundering Accounts

Tai, Chih-Hua; Kan, Tai-Jung

doi:10.1109/icsse.2019.8823264

Cited by 13 publications

(13 citation statements)

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“…Countries worldwide are now required to examine and prevent their risks related to virtual assets, financial activities and providers and ensure that they are licensed and subjected to being monitored by competent and credible national monetary authorities. As layering and integrating transactions are the main techniques of cryptocurrency laundering, insisting on a clear record in the Blockchain can further mitigate money laundering threats (Tai and Kan, 2019). When clear digital tracks represent a trail of verifiable transactions, it becomes much harder to hide the origins of the illegitimate digital currencies.…”

Section: Literature Reviewmentioning

confidence: 99%

“Cyber-laundering”: the change of money laundering in the digital age

Wronka¹

2021

JMLC

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Purpose This study aims to illustrate and determine how illegally obtained funds are laundered through online platforms and companies in different economic sectors in the digital age. Design/methodology/approach A qualitative analysis approach using purpose sampling methods, including 21 semi-structured interviews with prevention experts, compliance officers and convicted cybercriminals, resulted in the determination of concrete money-laundering methods involving the employment of online platforms provided by companies and institutions in different economic sectors. Findings The current study focuses on various companies in different economic segments that mitigate cyber laundering and the anti-money laundering measures that can be adopted. Therefore, this paper provides a detailed discussion and analysis on how money launderers avoid being detected. Both preventive and criminal perspectives are taken into consideration. Originality/value By identifying the gaps in the current anti-money-laundering mechanisms, it will provide compliance officers, legislators and law enforcement agencies with an in-depth insight into how cyber laundering operates in various economic sectors.

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Section: Literature Reviewmentioning

confidence: 99%

“Cyber-laundering”: the change of money laundering in the digital age

Wronka¹

2021

JMLC

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“…Most cases the criminal activities are dependent in money laundering process all over the fraud occurrences. However, because it is common in dirty money laundering, banking transfers or commercial transactions can be divided up into segments, leaving the challenge of manual detection of money laundering [4]. Money Laundering It can be defined, in general, as the illegal appearance of camouflage of income followthrough, or the use of criminal activity to conceal and source.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying Suspicious Money Laundering Transaction Based on Collaborative Relational Data Screening Model Using Decision Classifier in Transactional Database

2020

jcr

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Transactional data be most economical concern in banking sectors for financial prospects. Due to criminal activities and tolerant they had many issues specifically in money laundering. The illegal transaction, robbery, malpractice non related source of access, are tedious to identifying legal transactions. By the higher transactions using the big data analysis to identify possible money laundering activities. To resolve the issue to make effective future selection approach in transactions and classify the resultant under data cleaning, partial information from traditional statistical analysis, and data mining process. To propose an collaborative relational data screening model (CRDSCM) using decision classifier in transactional database. Autocorrelation function with screening cluster approach were conducted to examine the relationship between the attribute similarities on each transaction collaborative data analysis method. With support of time serious data including predictive analytics for decision making the detection of attitude and money laundering activities that are used to deliver classified result in efficient way

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“…In fact, the total loss of fraud in 2019 was 28.3 billion USD with very low clear-up rates as reported by the Communication Fraud Control Association. 1 Fund transfer fraud (FTF), such as romance scams, buyer overpays, etc., 2 is difficult to prevent and detect. Various fraud prevention acts have been enacted throughout the world [2], including (in Taiwan) the Proceeds of Crime Act, the Money Laundering Prevention Act, and the Money Laundering Control Act.…”

Section: Introductionmentioning

confidence: 99%

“…1 Fund transfer fraud (FTF), such as romance scams, buyer overpays, etc., 2 is difficult to prevent and detect. Various fraud prevention acts have been enacted throughout the world [2], including (in Taiwan) the Proceeds of Crime Act, the Money Laundering Prevention Act, and the Money Laundering Control Act.…”

Section: Introductionmentioning

confidence: 99%

“…In light of the above observations, we extend our conference work [10] to develop two new feature categories, namely, behavior and segmentation -generated based on characteristics belonging solely to (non-)fraudulent accounts informed by financial expertise. Such feature constructions can capture causeeffect relationships between modi operandi and features to 1 See https://cfca.org/wp-content/uploads/2021/02/CFCA-2019-Fraud-Loss-Survey.pdf 2 See https://www.worldremit.com/en/stories/story/2020/01/20/moneytransfer-scams improve interpretability; our later experiments show that the importance of these features ranks high, which reflects their strong relationship. Behavioral features capture critical transaction patterns that are typically used only by fraudsters (or normal users) and that cannot be properly captured by the construction guidelines of recency, frequency, monetary, and anomaly raised in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Feature Engineering and Resampling Strategies for Fund Transfer Fraud With Limited Transaction Data and a Time-Inhomogeneous Modi Operandi

Hsin

Dai

et al. 2022

IEEE Access

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Detecting financial fraud to profile crimes and pinpoint system vulnerabilities is an essential issue in the financial industry. Because of interpretability requirements and the lack of mass transaction data due to privacy regulations, sophisticated handcrafted features have been adopted in much of the literature for fraud detection. In addition to established recency, frequency, monetary, and anomaly features, we propose behavior-and segmentation-type features based on statistical characteristics belonging solely to (non-)fraudulent accounts informed by financial expertise. Our proposed features are difficult for automatic feature generators to synthesize, and provide transparent cause-effect relationships and good prediction results. Features with time-inhomogeneous properties cause popular boosting classifiers such as XGBoost and LGBM to produce unstable detection results. We use the Kolmogorov-Smirnov test to detect and remove these features to improve XGBoost and LGBM detection performance and robustness. The resulting performance shown in our experiments is better than that of other classifiers, such as SVM and random forests. We examine the advantage of our technique by comparing it with several feature engineering works on fraud detection and automatic feature generation methods. On the other hand, we also find that generating training/testing sets with random sampling falsely eliminates such time inhomogeneity and results in misleading assessments of the robustness of machine learning models. These time-inhomogeneous phenomena also entail various modus operandi patterns, which influence the performance of different resampling methods for addressing data imbalance in fraud detection. Improper linear interpolation of SMOTE-related approaches leads to poor performance due to varying patterns of modi operandi. However, synthesizing fraudulent samples with simple oversampling and GANs mitigates this problem.

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Identifying Money Laundering Accounts

Cited by 13 publications

References 0 publications

“Cyber-laundering”: the change of money laundering in the digital age

“Cyber-laundering”: the change of money laundering in the digital age

Identifying Suspicious Money Laundering Transaction Based on Collaborative Relational Data Screening Model Using Decision Classifier in Transactional Database

Feature Engineering and Resampling Strategies for Fund Transfer Fraud With Limited Transaction Data and a Time-Inhomogeneous Modi Operandi

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