Detecting Mobile Money Laundering Using Genetic Algorithm as Feature Selection Method with Classification Method

Bashir, Shamila; Ghous, Hamid

doi:10.5281/zenodo.5149794

2021

DOI: 10.5281/zenodo.5149794

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Detecting Mobile Money Laundering Using Genetic Algorithm as Feature Selection Method with Classification Method

Shamila Bashir¹,

Hamid Ghous²

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Predicting mobile money transaction fraud using machine learning algorithms

Lokanan

2023

Applied AI Letters

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The ease with which mobile money is used to facilitate cross-border payments presents a global threat to law enforcement in the fight against money laundering and terrorist financing. This paper aims to utilize machine learning classifiers to predict transactions flagged as a fraud in mobile money transfers. The data for this study were obtained from real-time transactions that simulate a well-known mobile transfer fraud scheme. Logistic regression is used as the baseline model and is compared with ensemble and gradient descent models. The results indicate that the logistic regression model still showed reasonable performance while not performing as well as the other models. Among all the measures, the random forest classifier exhibited outstanding performance. The amount of money transferred emerged as the top feature for predicting money laundering transactions in mobile money transfers. These findings suggest that further research is needed to enhance the logistic regression model, and the random forest classifier should be explored as a potential tool for law enforcement and financial institutions to detect money laundering activities in mobile money transfers.

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Predicting mobile money transaction fraud using machine learning algorithms

Lokanan

2023

Applied AI Letters

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The determinants of investment fraud: A machine learning and artificial intelligence approach

Lokanan

2022

Front. Big Data

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Investment fraud continues to be a severe problem in the Canadian securities industry. This paper aims to employ machine learning algorithms and artificial neural networks (ANN) to predict investment in Canada. Data for this study comes from cases heard by the Investment Industry Regulatory Organization of Canada (IIROC) between June 2008 and December 2019. In total, 406 cases were collected and coded for further analysis. After data cleaning and pre-processing, a total of 385 cases were coded for further analysis. The machine learning algorithms and artificial neural networks were able to predict investment fraud with very good results. In terms of standardized coefficient, the top five features in predicting fraud are offender experience, retired investors, the amount of money lost, the amount of money invested, and the investors' net worth. Machine learning and artificial intelligence have a pivotal role in regulation because they can identify the risks associated with fraud by learning from the data they ingest to survey past practices and come up with the best possible responses to predict fraud. If used correctly, machine learning in the form of regulatory technology can equip regulators with the tools to take corrective actions and make compliance more efficient to safeguard the markets and protect investors from unethical investment advisors.

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Analyzing public sentiments on the Cullen Commission inquiry into money laundering: harnessing deep learning in the AI of Things Era

Lokanan

2023

Front. Internet Things

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This study employs deep learning methodologies to conduct sentiment analysis of tweets related to the Cullen Commission’s inquiry into money laundering in British Columbia. The investigation utilizes CNN, RNN + LSTM, GloVe, and BERT algorithms to analyze sentiment and predict sentiment classes in public reactions when the Commission was announced and after the final report’s release. Results reveal that the emotional class “joy” predominated initially, reflecting a positive response to the inquiry, while “sadness” and “anger” dominated after the report, indicating public dissatisfaction with the findings. The algorithms consistently predicted negative, neutral, and positive sentiments, with BERT showing exceptional precision, recall, and F1-scores. However, GloVe displayed weaker and less consistent performance. Criticisms of the Commission’s efforts relate to its inability to expose the full extent of money laundering, potentially influenced by biased testimonies and a narrow investigation scope. The public’s sentiments highlight the awareness raised by the Commission and underscore the importance of its recommendations in combating money laundering. Future research should consider broader stakeholder perspectives and objective assessments of the findings.

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Detecting Mobile Money Laundering Using Genetic Algorithm as Feature Selection Method with Classification Method

Cited by 3 publications

References 5 publications

Predicting mobile money transaction fraud using machine learning algorithms

Predicting mobile money transaction fraud using machine learning algorithms

The determinants of investment fraud: A machine learning and artificial intelligence approach

Analyzing public sentiments on the Cullen Commission inquiry into money laundering: harnessing deep learning in the AI of Things Era

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