Breaking Through Opacity: A Context-Aware Data-Driven Conceptual Design for a Predictive Anti Money Laundering System

Hamid, Oussama H.

doi:10.1109/ieeegcc.2017.8448084

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…Embodied cognitive science is one promising approach, and there will be other applications that allow us to acknowledge problems and implement innovative solutions for acute societal problems such as combating money laundering (Hamid, 2017) or promoting green technologies for smart cities (Schaffers et al, 2011). The adoption of a "both and" view instead of an "either or" one as a guiding principle for research means that embodied cognition may contain representational, abstract, and symbolic aspects.…”

Section: Resultsmentioning

confidence: 99%

Embodied Cognition and Human-Machine Coexistence

Smith

Hamid

2017

UKH J SCI ENG

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A B S T R A C TRecently, we discussed the relative importance of direct perception, embodiment, metaphors, and ethics for cooperative human-machine coexistence. The present paper deepens the examination of embodiment and direct perception by considering differences between computational and representational models on one hand and embodied cognition on the other. We found that to achieve true artificial intelligence (AI) and, hence, a cooperative human-machine coexistence, research must overcome the limitations of computational and representational models. This can be reached by connecting machines to the world through bodies that exhibit sensory and motor skills as demonstrated by embodied cognition. Furthermore, substantial improvement in AI could be achieved by adopting a hybrid framework in which embodied cognition, for example, may contain representational, abstract, and symbolic aspects. The adoption of such a "both and" instead of "either or" view is a more realistic approach for progress in AI applications.

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Section: Resultsmentioning

confidence: 99%

Embodied Cognition and Human-Machine Coexistence

Smith

Hamid

2017

UKH J SCI ENG

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“…Zhou et al (2017) proposed a ProGuard technique to detect malicious accounts and activities. Using SNA, the authors proposed a method for fraud detection (Colladon & Remondi, 2017;Hamid, 2017;Shaikh & Nazir, 2018).…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we discuss our proposed system model and the problem formulation. Sahin, 2011;Snchez, 2009;Abdulla, 2015;Sarno, 2015;Nipane, 2016;HaratiNik, 2012;Behera, 2015;Halvaiee, 2014;Save, 2017;MishraA, 2017;Malini, 2017;Gaur, 2017;Xuan, 2018;Jadvani, 2018;Robinson, 2018Alimolaei, 2015Pouramirarsalani, 2017;El Orche, 2018Hamid, 2017Pooja, 2018;Shaikh, 2018;Suresh, 2016…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

A semantic rule based digital fraud detection

Ahmed

Ansar

Muckley³

et al. 2021

PeerJ Computer Science

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Digital fraud has immensely affected ordinary consumers and the finance industry. Our dependence on internet banking has made digital fraud a substantial problem. Financial institutions across the globe are trying to improve their digital fraud detection and deterrence capabilities. Fraud detection is a reactive process, and it usually incurs a cost to save the system from an ongoing malicious activity. Fraud deterrence is the capability of a system to withstand any fraudulent attempts. Fraud deterrence is a challenging task and researchers across the globe are proposing new solutions to improve deterrence capabilities. In this work, we focus on the very important problem of fraud deterrence. Our proposed work uses an Intimation Rule Based (IRB) alert generation algorithm. These IRB alerts are classified based on severity levels. Our proposed solution uses a richer domain knowledge base and rule-based reasoning. In this work, we propose an ontology-based financial fraud detection and deterrence model.

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“…(Adedoyin, Kapetanakis, Samakovitis, & Petridis, 2017);(Khanuja & Adane, 2018);(Hanbar, Shukla, Modi, & Vyjayanthi, 2019);(Plaksiy, Nikiforov, & Miloslavskaya, 2019);(Jin & Qu, 2018);(Hamid, 2017);(Li, Cao, Qiu, Zhao, & Zheng, 2017);(Prakash, Apoorva, Amulya, Kavya, & KN, 2019);(Plaksiy, Nikiforov, & Miloslavskaya, 2018); (El-Banna, Khafagy, & El Kadi, 2020);(El-Banna et al, 2020) …”

mentioning

confidence: 99%

Intelligent Anti-Money Laundering Fraud Control Using Graph-Based Machine Learning Model for the Financial Domain

Usman

Naveed

Munawar

2023

Journal of Cases on Information Technology

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Financial domains are suffering from organized fraudulent activities that are inflicting the world on a larger scale. Basel Anti-Money Laundering (AML) index enlists 146 countries, which are impacted by criminal acts like money laundering, and represents the country's risk level with a notable deteriorating trend over the last five years. Despite AML being a substantially focused area, only a fraction of such activities has been prevented. Because financial data related to this field is concealed, access is limited and protected by regulatory authorities. This paper aims to study a graph-based machine-learning model to identify fraudulent transactions using the financial domain's synthetic dataset (100K nodes, 5.3M edges). Graph-based machine learning with financial datasets resulted in promising 77-79% accuracy with a limited feature set. Even better results can be achieved by enriching the feature vector. This exploration further leads to pattern detection in the graph, which is a step toward AML detection.

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Breaking Through Opacity: A Context-Aware Data-Driven Conceptual Design for a Predictive Anti Money Laundering System

Cited by 8 publications

References 16 publications

Embodied Cognition and Human-Machine Coexistence

Embodied Cognition and Human-Machine Coexistence

A semantic rule based digital fraud detection

Intelligent Anti-Money Laundering Fraud Control Using Graph-Based Machine Learning Model for the Financial Domain

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