Anti Money Laundering detection using Naïve Bayes Classifier

Kumar, Ashwini; Das, Sanjoy; Tyagi, Vishu

doi:10.1109/gucon48875.2020.9231226

Cited by 8 publications

(10 citation statements)

References 16 publications

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“…: DATA SCIENCE TO IDENTIFY CRIMES AGAINST PUBLIC ADMINISTRATION service users. The article by [13] seeks to analyze financial transactions in order to find suspicious transactions that lead to money laundering, while [3] propose an ontology to, applied to a data warehouse, identify inconsistencies in payroll.…”

Section: Resultsmentioning

confidence: 99%

Data science to identify crimes against public administration

Souza,

Júnior,

Fontes

et al. 2023

Annals of Computer Science and Information Systems

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Context:The management of public resources is subject to illegal acts and the automatic identification of such acts depends on the analysis of a lot of data. Objective: The object of this work is the analysis of scientific publications through a study based on systematic mapping with the purpose of evaluating them in relation to the use of automated tools to identify crimes against public administration in databases from the perspective of researchers in the data science context. Method: Using PICO strategy (Population, In-tervention, Comparison, and Outcome), a systematic mapping was conducted to find the primary studies in the literature and collect evidence for directing future research. Results: Nineteen works were found that fit the proposed cri-teria. Almost 80% of the studies found seek to identify some type of fraud in bidding processes, obtaining accuracies between 72% and 99%. The research also revealed different techniques for approaching the problem. Considering all the works, the most used databases are bidding bases, lawsuits, public notices and corporate structure of companies, respectively. Conclusions: The work has shown a recent increase in interest in analyzing public data for irregularities. It is expected that this analysis will help control bodies elucidating different ways of detecting crimes against the public administration in an automated way.

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Section: Resultsmentioning

confidence: 99%

Data science to identify crimes against public administration

Souza,

Júnior,

Fontes

et al. 2023

Annals of Computer Science and Information Systems

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“…A. Machine Learning Process 1) Data acquisition: A financial dataset is transactions with fields such as the amount, the date, the beneficiary [6], [7], [8]. They are confidential, and the lack of public data hinders experiments, particularly for their validation and comparison.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised Method to Detect Fraudulent Transactions and Identify Fraud Types while Minimizing Mounting Costs

Hamza¹,

Abrouk²,

Cullot³

et al. 2023

IJACSA

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Financial fraud is a complex problem faced by financial institutions, and existing fraud detection systems are often insufficient, resulting in significant financial losses. Researchers have proposed various machine learning-based techniques to enhance the performance of these systems. In this work, we present a semi-supervised approach to detect fraudulent transactions. First, we extract and select features, followed by the training of a binary classification model. Secondly, we apply a clustering algorithm to the fraudulent transactions and use the binary classification model with the SHAP framework to analyze the clusters and associate them with a particular fraud type. Finally, we present an algorithm to detect and assign a fraud type by leveraging a multi-fraud classification model. To minimize the mounting cost of the model, we propose an algorithm to choose an optimal threshold that can detect fraudulent transactions. We work with experts to adapt a risk cost matrix to estimate the mounting cost of the model. This risk cost matrix takes into account the cost of missing fraudulent transactions and the cost of incorrectly flagging a legitimate transaction as fraudulent.In our experiments on a real dataset, our approach achieved high accuracy in detecting fraudulent transactions, with the added benefit of identifying the fraud type, which can help financial institutions better understand and combat fraudulent activities. Overall, our approach offers a comprehensive and efficient solution to financial fraud detection, and our results demonstrate its effectiveness in reducing financial losses for financial institutions.

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“…Zhang et al 5 balanced data distribution by applying undersampling and oversampling methods on real transaction data from US financial institutions. Kumar et al 6 used a Naive Bayes Classifier to identify money laundering behaviors from 10,000 transaction data. Senator et al 7 used XGBoost to identify money laundering transactions and reduced training time by utilizing GPUs.…”

Section: Related Workmentioning

confidence: 99%

Anti-money laundering method based on hierarchical risk control knowledge graph

Tang,

Sun

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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With the development of the Internet and advances in technology, financial transactions have become more convenient, but money laundering has also become more concealed, complex, and can be quickly adjusted according to regulatory policies, making traditional methods based on rule libraries and blacklists difficult to cope with the current Anti-Money Laundering (AML) challenges. To improve the efficiency and accuracy of AML work, various machine learning methods have been proposed. However, these methods are often difficult to be widely used due to their complex models and poor interpretability. To overcome the shortcomings of existing methods and adapt to the changing trends in money laundering while ensuring good interpretability, this paper proposes an AML method based on Hierarchical Risk Control Knowledge Graph (HRCKG). Firstly, a simulated AML dataset is generated based on a small amount of real data and money laundering patterns. Next, various transaction features are extracted to construct graded risk control indicators, which are used to automatically search for and filter risk control rules to form a rule library, and then construct an HRCKG. Finally, integrating rule-based reasoning and graph-based reasoning, this paper uses rule matching, node importance measurement, and community discovery algorithms to evaluate the money laundering risks of accounts, identify money laundering accounts, and discover money laundering groups. Through comparison with other machine learning methods and analysis of experimental results, the effectiveness and application value of the proposed method is demonstrated.

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Anti Money Laundering detection using Naïve Bayes Classifier

Cited by 8 publications

References 16 publications

Data science to identify crimes against public administration

Data science to identify crimes against public administration

Semi-supervised Method to Detect Fraudulent Transactions and Identify Fraud Types while Minimizing Mounting Costs

Anti-money laundering method based on hierarchical risk control knowledge graph

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