Transaction Fraud Detection Based on Total Order Relation and Behavior Diversity

Zheng, Lutao; Liu, Guanjun; Yan, Chao; Jiang, Changjun

doi:10.1109/tcss.2018.2856910

Cited by 72 publications

(26 citation statements)

References 22 publications

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“…Background transaction security processing is illustrated in Figure 7 and Figure 8. The risk model refers to the random forest algorithm in the literature [19] and the neural network model of transaction fraud detection, as well as behavior detection algorithm [20]. The specific algorithm part will not be analyzed in detail in this article.…”

Section: Business Transaction Security Analysismentioning

confidence: 99%

Overview of Intelligent Online Banking System Based on HERCULES Architecture

Luo

Peng

2020

IEEE Access

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The online banking transaction system is the application system with the most complex business, the most demanded, and frequent version updates in the software engineering application system. The existing online banking business sub-module is intelligent and faces major challenges in security. Traditional online banking systems cannot meet this capability. This article combines machine learning and online banking business module design to implement a business agent online banking system based on a new architecture. The article first proposes new features and new challenges of the online banking system, discusses the technical problems solved by the intelligent online banking system, and analyzes HERCULES Architecture business intelligent machine learning algorithm model, smart deposits and white-collar loans and other core processes, and then designed and implemented an intelligent online banking system business model, focusing on the issues of the intelligent online banking system, soft load balancing implementation and transaction security, through business implementation The effectiveness of the proposed business agent is verified. The actual use results show that the intelligent online banking of the HERCULES architecture has greatly improved the intelligence and security of the traditional online banking system. Finally, we summarize and analyze the value and innovation of the intelligent online banking system, and look forward to the shortcomings of the system.

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Section: Business Transaction Security Analysismentioning

confidence: 99%

Overview of Intelligent Online Banking System Based on HERCULES Architecture

Luo

Peng

2020

IEEE Access

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“…Mengingat maraknya penipuan pada situs e-commerce yang dapat mengakibatkan kerugian finansial yang cukup besar, sebagai konsumen perlu adanya pengetahuan mengenai jenis penipuan yang umum terjadi dan metode pencegahan yang digunakan untuk mendeteksi penipuan agar terhindar dari berbagai kerugian. Beberapa penelitian sebelumnya hanya membahas tentang identifikasi dan metode pencegahan penipuan e-commerce ( Makarti, 2011;Chang & Chang, 2012;Syed & Shabbir, 2013;Valentin, 2013;Caldeira, Brandao, & Pereira, 2014;Leung, Lai, Chen, & Wan, 2014;Massa & Valverde, 2014;Hwang & Lai, 2015;JRana & Baria, 2015;Singh & Singh, 2015;Abdallah, Maarof, & Zainal, 2016;Beránek, Nýdl, & Remeš, 2016;Gerlach, Pavlovic, & Gerlach, 2016;Lima & Pereira, 2016;Yang et al, 2016;Ramadhan & Amelia, 2016;Sun et al, 2017;Prisha, Neo, Ong, & Teo, 2017;Raghava-Raju, 2017;Shaji & Panchal, 2017;Wiralestari, 2017;Renjith, 2018;Weng et al, 2018;Zhao et al, 2018;Zheng et al, 2018); Amasiatu Amiruddin et al, 2019;Carta et al, 2019;Raghavan & Gayar, 2019;Shah et al, 2019;Soomro et al, 2019. Sementara penelitian lainnya lebih fokus pada penipuan sistem pembayaran dan penipuan terkait dengan pelanggan (Keraf & Hidup, 2010;Rofiq & Mula, 2010;Raj & Portia, 2011;Hu, Liu, & Sambamurthy, 2011;…”

Section: Pendahuluanunclassified

Pemetaan Bentuk Dan Pencegahan Penipuan E-Commerce

Dewi

Mahyuni

2020

EEB

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Seiring berkembangnya transaksi jual beli, penipuan elektronik juga turut meningkat sehingga mengakibatkan banyak konsumen yang telah mengalami kerugian akibat penipuan yang terjadi. Tujuan dari penelitian ini adalah untuk mengetahui jenis penipuan yang dapat terjadi dalam perdagangan elektronik dan pencegahan yang dapat dilakukan. Dalam penelitian ini disajikan review dengan metode pendekatan interpretif atas artikel terkait, dengan proses pemetaan pada artikel yang dikumpulkan melalui situs Google Cendekia, Elsevier, Springer, Taylor & Francis, dan MDPI (Multidisciplinary Digital Publishing Institute). Dari sumber tersebut, 105 artikel berhasil dikumpulkan, setelah proses seleksi artikel berdasarkan 10 tahun terahir dan kesesuaian pembahasan akhirnya diperoleh 55 artikel. Hasil penelitian ini adalah ditemukan berbagai jenis penipuan pada keempat kategori e-coomerce serta penipuan pada sistem pebayaran dan penipuan pada e-commerce yang menyangkut pelanggan. Metode modern pendeteksi penipuan juga disajikan dalam penelitian ini, seperti data mining, jaringan bayesan, algoritma, mesin pendukung vector, pemrograman genetik, pohon pengambilan keputusan, Adaptive Neuro-Fuzzy Inference System, Situs Web Bantuan Perlindungan (PAW), dan Model Privacy Antecedent-Privacy Concern-Outcomes (APCO). Dengan penjabaran pada hasil penelitian ini, konsumen diharapkan untuk lebih berhati-hati saat melakukan transaksi di situs e-commerce agar terhindar dari berbagai tindak penipuan.

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“…Zheng et al [30] demonstrate how user's behavior profile can be represented in a form of a graph, where vertexes represent different values of certain transaction feature and weighted edges represent the correlation between them, i.e. how likely value of one attribute would be present if the value of another is.…”

Section: Fraud Discovery Approachesmentioning

confidence: 99%

Fraud Detection in Payments Transactions: Overview of Existing Approaches and Usage for Instant Payments

Diadiushkin

Sandkuhl

Maiatin

2019

CSIMQ

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Financial industries are undergoing a digital transformation of their products, services, overall business models. Part of this digitalization in banking aims at automating most of the manual work in payment handling and integrating the workflows of involved service providers. The focus of the work presented in this paper is on fraud discovery and steps to fully automate it. Fraud discovery in financial transactions has become an important priority for banks. Fraud is increasing significantly with the expansion of modern technology and global communication, which results in substantial damages for the banks. Instant payment (IP) transactions cause new challenges for fraud detection due to the requirement of short processing time. The paper investigates the possibility to use artificial intelligence in IP fraud detection. The main contributions of our work are (a) an analysis of problem relevance from business and literature perspective, (b) a proposal for technological support for using AI in fraud detection of instant payment transactions, and (c) a feasibility study of selected fraud detection approaches.

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Transaction Fraud Detection Based on Total Order Relation and Behavior Diversity

Cited by 72 publications

References 22 publications

Overview of Intelligent Online Banking System Based on HERCULES Architecture

Overview of Intelligent Online Banking System Based on HERCULES Architecture

Pemetaan Bentuk Dan Pencegahan Penipuan E-Commerce

Fraud Detection in Payments Transactions: Overview of Existing Approaches and Usage for Instant Payments

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