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Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud
[article]
2020
arXiv
pre-print
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We tested the deep forest model on an extra-large scale task, i.e., automatic detection of cash-out fraud, with more than 100 millions of training samples. ...
Deep forest is a recently proposed deep learning framework which uses tree ensembles as its building blocks and it has achieved highly competitive results on various domains of tasks. ...
ACKNOWLEDGMENTS The authors want to thank reviewers for their helpful comments and suggestions. ...
arXiv:1805.04234v3
fatcat:iwrny7pogvcezfz6ukvjcg35tu
Detection of Fraud Transactions Using Recurrent Neural Network during COVID-19
2020
Journal of Advanced Research in Medical Science & Technology
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The objective of this paper is to propose a method to detect such fraud transactions during such unmanageable situation of the pandemic. ...
The proposed method is capable to detect deceptive transactions with an accuracy of 99.87%, F1-Score of 0.99 and MSE of 0.01. ...
transactions for transfer and cash-out type transaction J. ...
doi:10.24321/2394.6539.202012
fatcat:ysaoga4wyfhela4o6bjdz3jxc4
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods
[article]
2020
arXiv
pre-print
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2020
Computer Science & Information Technology
unpublished
doi:10.5121/csit.2020.101018 fatcat:clewmedj2nfnzotkgizmqrnriy
doi:10.5121/csit.2020.101018 fatcat:clewmedj2nfnzotkgizmqrnriy
Credit card fraud is an ongoing problem for almost all industries in the world, and it raises millions of dollars to the global economy each year. ...
The ultimate goal of this paper is to summarize state-of-the-art approaches to fraud detection using artificial intelligence and machine learning techniques. ...
They mainly focus on the credit card cash-out problem. It is a fraud technique that spends all limit on the credit card. ...
arXiv:2007.14622v1
fatcat:jt4pzmk6qjcr3jhng2orv5opvm
An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication
2021
Wireless Communications and Mobile Computing
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It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. ...
This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. ...
It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. ...
doi:10.1155/2021/6079582
fatcat:a7wceyefgjbldi7vsr4rxo5rw4
Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques
2021
Security and Communication Networks
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(KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. ...
In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor ...
, for the credit card fraud dataset and description related to it in the public domain. ...
doi:10.1155/2021/9293877
fatcat:lattsosu2je2zk37v2czq32drq
A Survey of Online Card Payment Fraud Detection using Data Mining-based Methods
[article]
2020
arXiv
pre-print
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Forty-five peer-reviewed papers published in the domain of card fraud detection between 2009 and 2020 were intensively reviewed to develop this paper. ...
(and fraud detection) into their work , (ii) the feature engineering techniques that focus on cardholder behavioural profiling to separate fraudulent activities happening with the same card, and (iii) ...
[48] tested the performance of a CART based tree ensemble (which uses the Gini impurity of attributes to distribute data) against Random Forest to detect credit card fraud. ...
arXiv:2011.14024v1
fatcat:ewrgcogmdfewflp65jjpyxtjmm
Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications
2021
Sensors
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The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. ...
They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. ...
Acknowledgments: The constructive feedback of the anonymous reviewers was well-received and is hereby acknowledged.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21051594
pmid:33668773
fatcat:e4d4zx7f7zg4jgddjm565zwsgu
Deep learning and explainable artificial intelligence techniques applied for detecting money laundering – a critical review
2021
IEEE Access
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Future research directions are, application of XAI techniques to bring-out explainability, graph deep learning using natural language processing (NLP), unsupervised and reinforcement learning to handle ...
Literature shows the usage of statistical methods, data mining and Machine Learning (ML) techniques for money laundering detection, but limited research on Deep Learning (DL) techniques, primarily due ...
[23] proposed the unsupervised deep learning model to classify Brazilian exporters to find out the possibility of committing the frauds in exports. ...
doi:10.1109/access.2021.3086230
fatcat:n4wwkfoiaff5rjpelnddtcruwu
Predictive Fraud Analysis Applying the Fraud Triangle Theory through Data Mining Techniques
2022
Applied Sciences
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After benchmarking topic modeling techniques and supervised and deep learning classifiers, we find that LDA, random forest, and CNN have the best performance in this scenario. ...
In addition, although efforts have been made to detect fraud using machine learning, such actions have not considered the component of human behavior when detecting fraud. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app12073382
fatcat:b4l7644t3fgvtic3mabsdmqxg4
Quick survey of graph-based fraud detection methods
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:1910.11299v2 fatcat:iaglvgagfnhzxklgfgyrnu5cfuOther Versions
In general, anomaly detection is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected profiles. ...
We present a survey on anomaly detection techniques used for fraud detection that exploit both the graph structure underlying the data and the contextual information contained in the attributes. ...
The test show that the model is robust in automatically detecting multiple fraud blocks without predefining the block number, in comparison with other state-of-the-art baselines (which do no rely on deep ...
arXiv:1910.11299v3
fatcat:zyupd4ezxrgw3f7g5utzihy6qi
Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms
2022
IEEE Access
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People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. ...
However, due to low accuracy, there is still a need to apply state of the art deep learning algorithms to reduce fraud losses. ...
However, only a few studies have examined the application of deep neural networks in identifying CCF. [3] . It uses a number of deep learning algorithms for detecting CCF. ...
doi:10.1109/access.2022.3166891
fatcat:7xu45n2xpnauhp2ju4x23sx5cm
Classification of Maize leaf diseases from healthy leaves using Deep Forest
2020
Journal of Artificial Intelligence and Systems
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This paper proposes the recognition and classification of maize plant leaf diseases by application of the Deep Forest algorithm. ...
It justifies its low dependency on extensive Hyper-parameter tuning and the size of the dataset as against other Deep Learning Models based on neural networks. ...
They tested out their proposed algorithm on the problem of cash-out fraud detection, which has more than 100 million samples. ...
doi:10.33969/ais.2020.21002
fatcat:4ktgqcaio5gpvk7h34z4qum7o4
The accuracy versus interpretability trade-off in fraud detection model
2021
Data & Policy
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This paper first focuses on the translation of practical questions raised in the banking industry at each step of the fraud management process into performance evaluation required to design a fraud detection ...
phenomenon, and the unavoidable trade-off between accuracy and interpretability of detection. ...
The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ...
doi:10.1017/dap.2021.3
fatcat:dceavgt2wbhujdrndtwrt5fppa
Facilitating User Authorization from Imbalanced Data Logs of Credit Cards Using Artificial Intelligence
2020
Mobile Information Systems
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In case of fraud transaction, an alert can be sent to the related financial organization that can suspend the release of payment for particular transaction. ...
Of all the machine learning models such as RUSBoost, decision tree, logistic regression, multilayer perceptron, K-nearest neighbor, random forest, and support vector machine, the overall predictive performance ...
To improve detection efficiency, computerized and automatic FDSs were developed. ...
doi:10.1155/2020/8885269
fatcat:4gubbxq4jbcndhfcnjx5gwajbe
Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape
2021
IEEE Access
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go to extreme lengths to cash-out their
[80] G. ...
Whitty, “Automatically dismantling online dating fraud,” arXiv, buried-treasure-criminals-to-go-to-extreme-lengths-to-cash-out-crypto
vol. 15, pp. 1128–1137, 2019 ...
doi:10.1109/access.2021.3134076
fatcat:lm2upcaoabbnbie6r4sfzhjh4y
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