Improving Money Laundering Detection Using Optimized Support Vector Machine

As customers tendencies learn rapidly about online transactions due to the outbreak of the Covid-19 pandemic, fraudulent transactions that mimic legitimate transactions will continue to increase unabated. Over the years, fraudulent transactions have been committed but the supervised and unsupervised machine learning classifiers and fraud detection techniques could not stop the fraud from occurring at the onset. However, fraudulent cases are detected after post-occurrence thereby recognizing these techniques as non-smart detection models because fraud could only be detected using past data sets. Various fraud detection or classifier techniques using machine learning techniques such as decision trees, random forests, k-means, and neural networks have been studied. Also, the possibility of overfitting due to the branch growth associated with the decision trees is a classifier performance problem. The application of the probability axiomatic metric (PAM) to determine the accuracy of the models is a classification evaluation problem because of the relatively moderate error rate. To overcome the above shortfalls, we proposed a threshold Mahalanobis distance (TMD) classifier and adopted BETH as a performance evaluation metric. The result revealed that TMD is comparable to neural network (NN), k-means, isolation forest, XGBoost, and random forest based on the credit card data set. The analysis showed that TMD and NN have higher efficiency compared to other classifiers. This study demonstrated that the TMD classifier is unique and could be applied to extract legitimate and illegitimate transactions from customer transaction data sets. The study concludes that TMD is robust and comparable to NN and the classification errors associated with BETH are minimal compared to PAM for both TMD and NN.

The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management

Background: Fraud in financial transaction is at the root of corruption issues recorded in organization. Detecting fraud practices has become increasingly complex and challenging. As a result, auditors require precise analytical tools for fraud detection. Grouping financial transaction data using K-Means Clustering algorithm can enhance the efficiency of applying Benford Law for optimal fraud detection. Objective: This study aimed to introduce Multiple Benford Law Model for the analysis of data to show potential concealed fraud in the audited organization financial transaction. The data was categorized into low, medium, and high transaction values using K-Means Clustering algorithm. Subsequently, it was reanalyzed through Multiple Benford Law Model in a specialized fraud analysis tool. Methods: In this study, the experimental procedures of Multiple Benford Law Model designed for public sector organizations were applied. The analysis of suspected fraud generated by the toolkit was compared with the actual conditions reported in audit report. The financial transaction dataset was prepared and grouped into three distinct clusters using the Euclidean distance equation. Data in these clusters was analyzed using Benford Law, comparing the frequency of the first digit’s occurrence to the expected frequency based on Benford Law. Significant deviations exceeding ±5% were considered potential areas for further scrutiny in audit. Furthermore, the analysis were validated by cross-referencing the result with the findings presented in the authorized audit organization report. Results: Multiple Benford Law Model developed was incorporated into an audit toolkit to automated calculations based on Benford Law. Furthermore, the datasets were categorized using K-Means Clustering algorithm into three clusters representing low, medium, and high-value transaction data. Results from the application of Benford Law showed a 40.00% potential for fraud detection. However, when using Multiple Benford Law Model and dividing the data into three clusters, fraud detection accuracy increased to 93.33%. The comparative results in audit report indicated a 75.00% consistency with the actual events or facts discovered. Conclusion: The use of Multiple Benford Law Model in audit toolkit substantially improved the accuracy of detecting potential fraud in financial transaction. Validation through audit report showed the conformity between the identified fraud practices and the detected financial transaction. Keywords: Fraud Detection, Benford’s Law, K-Means Clustering, Audit Toolkit, Fraudulent Practices.

In this digital world, numerous credit card-based transactions take place all over the world. Concomitantly, gaps in process flows and technology result in many fraudulent transactions. Owing to the spurt in the number of reported fraudulent transactions, customers and credit card service providers incur significant financial and reputation losses respectively. Therefore, building a powerful fraud detection system is paramount. It is noteworthy that fraud detection datasets, by nature, are highly unbalanced. Consequently, almost all of the supervised classifiers, when built on the unbalanced datasets, yield high false negative rates. But, the extant oversampling methods while reducing the false negatives, increase the false positives. In this paper, we propose a novel data oversampling method using Generative Adversarial Network (GAN). We use GAN and its variant to generate synthetic data of fraudulent transactions. To evaluate the effectiveness of the proposed method, we employ machine learning classifiers on the data balanced by GAN. Our proposed GAN-based oversampling method simultaneously achieved high precision, F1-score and dramatic reduction in the count of false positives compared to the state-of-the-art synthetic data generation based oversampling methods such as Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN) and random oversampling. Moreover, an ablation study involving the oversampling based on the ensemble of SMOTE and GAN/WGAN generated datasets indicated that it is outperformed by the proposed methods in terms of F1 score and false positive count.

Fraud in financial transactions is a critical issue for businesses, governments, and consumers, causing substantial financial losses and eroding trust in financial systems. Rule-based systems and other conventional fraud detection techniques have trouble identifying complex fraudulent activity. This research applies various machine learning (ML) algorithms to detect fraud in financial transactions. The research compares the performance of supervised ML techniques, such as random forest, logistic regression, and decision tree classifier, using a publicly available Kaggle dataset. It evaluates the models based on accuracy, precision, recall, and F1 scores. To address data imbalance in both undersampling and oversampling techniques, with a focus on oversampling techniques such as Synthetic Minority Over-Sampling Method (SMOTE) to enhance model performance. Results show that the Decision Tree Classifier, when used with oversampling, outperforms other models, achieving a 99% accuracy in detecting fraudulent transactions. This highlights the effectiveness of using ensemble and hybrid models in combination with oversampling to enhance the identification of fraud. The findings emphasize the importance of using advanced ML techniques and robust data preprocessing for detection of financial fraud.

The area of corporate bankruptcy prediction attains high economic importance, as it affects many stakeholders. The prediction of corporate bankruptcy has been extensively studied in economics, accounting and decision sciences over the past two decades. The corporate bankruptcy prediction has been a matter of talk among academic literature and professional researchers throughout the world. Different traditional approaches were suggested based on hypothesis testing and statistical modeling. Therefore, the primary purpose of the research is to come up with a model that can estimate the probability of corporate bankruptcy by evaluating its occurrence of failure using different machine learning models. As the dataset was not well prepared and contains missing values, various data mining and data pre-processing techniques were utilized for data preparation. Within this research, the task of resolving the issues induced by the imbalance between the two classes is approached by applying different data balancing techniques. We address the problem of imbalanced data with the random undersampling and Synthetic Minority Over Sampling Technique (SMOTE). We used five machine learning models (support vector machine, J48 decision tree, Logistic model tree, random forest and decision forest) to predict corporate bankruptcy earlier to the occurrence. We use data from 2009 to 2013 on Poland manufacturing corporates and selected the 64 financial indicators to be broken down. The main finding of the study is a significant improvement in predictive accuracy using machine learning techniques. We also include other economic indicators ratios, along with Altman’s Z-score variables related to profitability, liquidity, leverage and solvency (short/long term) to propose an efficient model. Machine learning models give better results while balancing the data through SMOTE as compared to random undersampling. The machine learning technique related to decision forest led to 99% accuracy, whereas support vector machine (SVM), J48 decision tree, Logistic Model Tree (LMT) and Random Forest (RF) led to 92%, 92.3%, 93.8% and 98.7% accuracy, respectively, with all predictive financial indicators. We find that the decision forest outperforms the other techniques and previous techniques discussed in the literature. The proposed method is also deployed on the web to assist regulators, investors, creditors and scholars to predict corporate bankruptcy.

<title>Abstract</title> Credit card fraud detection remains a critical yet challenging task due to the extreme class imbalance inherent in transaction datasets, where fraudulent activities constitute only a small fraction of the total records. To address this imbalance and enhance the detection of rare fraud instances, this study proposes a novel hybrid framework that integrates density-based clustering for data augmentation with an ensemble classification strategy optimized for high recall. In the preprocessing stage, the proposed method utilizes Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify minority-class clusters and synthetically augment the fraud class. This step aims to preserve the intrinsic structure of fraudulent patterns while increasing their representation in the training set. Subsequently, an ensemble model comprising Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) classifiers is constructed. Final predictions are generated using a Disjunctive Voting Ensemble (DVE) strategy, wherein a transaction is classified as fraudulent if any of the base classifiers predicts it as such. This permissive voting mechanism prioritizes recall, thereby minimizing the risk of undetected fraudulent transactions. Extensive experiments conducted on three publicly available credit card fraud imbalanced datasets containing transaction records from European credit cardholders in 2023, providing a realistic fraud detection scenario. Computational efficiency in both training and testing is achieved using the Anaconda Navigator (Spider-Python 3.12) environment. The comparative analysis shows that the proposed DBSCAN-augmented DVE framework delivers notable improvements over traditional ensemble approaches and single-model baselines, particularly in recall and F1-score, while preserving consistently high precision. This approach proves to be both robust and interpretable, making it highly applicable to real-world fraud detection scenarios characterized by severe class imbalance. The results are especially compelling, achieving recall and F1-scores as high as 99.5% and 99.8% respectively, and consistently maintaining a perfect accuracy and precision of 100% across all imbalanced datasets. This study highlights the effectiveness of hybrid ensemble approaches in combating credit card fraud. The findings lay the groundwork for developing more resilient and adaptive fraud detection systems, which are crucial in countering the evolving tactics of fraudsters. The proposed model marks a significant advancement in securing financial transactions and mitigating risks in an increasingly digital economy.

Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier

Fraudulent transactions and the methods to detect them are an an important issue for financial organizations globally. The requirement for progressive fraud detection systems to protect properties and maintain customer trust is predominant for financial organizations, but particular factors make the development of efficient and effective fraud detection models a challenge. Deep Learning (DL) has greatly improved fraud detection accuracy by detecting intrinsic patterns, whereas interpretability techniques improve transparency and build trust by making predictions understandable to experts. This study presents a Fraud Detection System using Recursive Feature Elimination and Waterwheel Plant Optimization (FDS-RFEWPO) model for financial transactions. The aim is to perform a comprehensive evaluation of fraud detection in high-dimensional financial transactions using advanced techniques. Initially, the FDS-RFEWPO technique follows min-max-based data pre-processing to normalize the input data. For the feature selection process, the FDS-RFEWPO model employs the Recursive Feature Elimination (RFE) technique to select the most relevant features from the dataset. Furthermore, the Variational Autoencoder/Wasserstein Autoencoder (VAE/WAE) model is employed for fraud detection and classification. To further enhance model performance, the Waterwheel Plant Optimization (WPO) technique is employed for hyperparameter tuning, ensuring the selection of optimal parameters that contribute to improved accuracy. Finally, the Explainable Artificial Intelligence (XAI) technique applies Local Interpretable Model-Agnostic Explanations (LIME) to improve the transparency, interpretability, and trustworthiness of Artificial Intelligence (AI) methods by making their decision-making procedures clear to humans. To evaluate the performance of the FDS-RFEWPO model, a comprehensive experimental analysis is conducted using a financial fraud detection dataset. The comparison study of the FDS-RFEWPO model demonstrates a superior accuracy of 97.41% over existing techniques.

Classification of imbalanced data set is a challenging problem as it is very difficult to achieve good classification accuracy for each class in case of imbalanced data sets. This problem arises in many real world applications like medical diagnosis of rare medical disease, fraud detection in financial domain, and faulty area detection in network troubleshooting etc. The imbalanced data set consists of small number of instances of minority classes and large number of instances of majority classes. Overall classification accuracy is computed by taking the ratio of correctly classified instances to total number of instances in a data set. For imbalanced data sets, correct classification of minority class instances contribute minimum in improvement of overall classification accuracy as compared to classification of majority class instances. Conventional classification techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM), and Naïve Bayes (NB) consider overall classification accuracy of the classifier only and thus evolve biased classifiers in case of imbalanced data set. However, instances of minority classes may contain rare but important information in many real world data sets. Thus, a classification technique that provides good classification accuracy on both minority and majority classes is needed. This paper proposes a combination of Multi Objective Genetic Programming (MOGP) and probability based Gaussian classifier for classification of imbalanced data set. MOGP considers classification accuracy of each class as separate objective and not the overall accuracy as single objective. Gaussian classifier is generative classifier in which distribution of one class never affect the classification of instances of other classes. The proposed methodology is applied on classification of imbalanced data sets from medical, life science, automobile, and space science domain. The results suggest that MOGP classifier outperformed other conventional classifiers (ANN, SVM, and NB) on tested imbalanced data sets.

This study explores the transformative role of artificial intelligence (AI), particularly machine learning (ML), in enhancing the detection and prevention of money laundering and fraud within the U.S. financial industry. The study aims to analyze how AI-driven techniques can significantly improve the accuracy, efficiency, and scalability of fraud detection systems. The study focuses on examining various machine learning algorithms, including supervised techniques like logistic regression and decision trees, as well as unsupervised methods such as clustering and anomaly detection. These techniques are utilized to analyze historical data, detect patterns, and identify suspicious transactions or fraudulent behaviors in real-time. The research method includes a comprehensive review of existing case studies and literature on AI applications in fraud detection, highlighting successful implementations of ML models in financial institutions. The findings reveal that machine learning models, such as random forests and support vector machines, have proven effective in detecting and preventing fraudulent activities with high precision and recall rates. Furthermore, the integration of AI with real-time data analysis capabilities enables continuous monitoring and immediate detection of irregularities. The study concludes that financial institutions in the U.S. must leverage AI advancements to enhance risk management systems, improve fraud detection, and mitigate the risks of money laundering. By adopting machine learning algorithms, financial organizations can stay ahead of emerging threats, ensuring the security of their operations and customer assets. Keywords: Artificial Intelligence, Machine Learning, Money Laundering, Fraud Detection, U.S. Financial Industry.

Imbalanced data is a challenge for classification models. It reduces the overall performance of traditional learning algorithms. Besides, the minority class of imbalanced datasets is misclassified with a high ratio even though this is a crucial object of the classification process. In this paper, a new model called the Lasso-Logistic ensemble is proposed to deal with imbalanced data by utilizing two popular techniques, random over-sampling and random under-sampling. The model was applied to two real imbalanced credit data sets. The results show that the Lasso-Logistic ensemble model offers better performance than the single traditional methods, such as random over-sampling, random under-sampling, Synthetic Minority Oversampling Technique (SMOTE), and cost-sensitive learning.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

Financial fraud, such as money laundering, is known to be a serious process of crime that makes illegitimately obtained funds go to terrorism or other criminal activity. This kind of illegal activities involve complex networks of trade and financial transactions, which makes it difficult to detect the fraud entities and discover the features of fraud. Fortunately, trading/transaction network and features of entities in the network can be constructed from the complex networks of the trade and financial transactions. The trading/transaction network reveals the interaction between entities, and thus anomaly detection on trading networks can reveal the entities involved in the fraud activity; while features of entities are the description of entities, and anomaly detection on features can reflect details of the fraud activities. Thus, network and features provide complementary information for fraud detection, which has potential to improve fraud detection performance. However, the majority of existing methods focus on networks or features information separately, which does not utilize both information. In this paper, we propose a novel fraud detection framework, CoDetect, which can leverage both network information and feature information for financial fraud detection. In addition, the CoDetect can simultaneously detecting financial fraud activities and the feature patterns associated with the fraud activities. Extensive experiments on both synthetic data and real-world data demonstrate the efficiency and the effectiveness of the proposed framework in combating financial fraud, especially for money laundering.

&lt;span lang="EN-US"&gt;Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and &lt;br /&gt; re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the &lt;br /&gt; lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.&lt;/span&gt;

Percutaneous coronary intervention (PCI) can be complicated by major adverse cardiovascular events (MACE; death, myocardial infarction [MI], and target vessel revascularization). Statistical models facilitated by machine learning (ML) can improve prediction of MACE compared with conventional models. However, two key challenges impair ML performance: (1) class imbalance in outcome distributions; and (2) bias affecting fairness across social subgroups. These challenges are amplified in small datasets. Additionally, no published ML model specifically addresses the influence of social determinants of health (SDoH) on PCI outcomes. We hypothesized that random oversampling would improve sensitivity and fairness when assessing the effect of SDoH on select PCI-associated MACE in a small dataset. We employed multivariable logistic regression to predict 180-day MACE and new MI following urgent PCI in a small dataset (N = 481). Three SDoH were pre-specified variables: race, marital status, and socioeconomic status at extremes of the spectrum (uninsured and Medicaid versus private insurance). Random oversampling was applied to the SDoH and outcomes while maintaining a consistent ratio between positive and negative outcomes. In the imbalanced dataset, the logistic regression revealed no significant association between SDoH and 180-day MACE or new MI (minimum P-value = 0.47). Although sensitivity for event detection was low (≤ 0.26), other metrics-including positive predictive value (PPV)-were commendable (≥ 0.80). ML classifiers trained on oversampled data for race and marital status showed increased sensitivity but decreased PPV as the ratio of adverse-to-favorable outcome increased. Other performance metrics remained stable. Equalized odds disparity decreased with increasing oversampling ratio, indicating improved fairness. However, for socioeconomic-extremes status, the low numbers of adverse events and small sub-group size limited the effectiveness of oversampling. The optimal oversampling ratio cut-point across all outcomes and SDoH was 0.30-0.40 (sensitivity-0.50; PPV-0.52). In small, imbalanced datasets, random oversampling can improve sensitivity and fairness of ML classifiers when evaluating PCI outcomes across race and marital status. However, this improvement comes at the expense of decreased PPV. The decrease in equalized odds disparity indicates improved balance in the performance of the classifiers across these two subgroups.