Bank Fraud Detection using Community Detection Algorithm

Sarma, Dhiman; Alam, Wahidul; Saha, Ishita; Alam, Mohammad Jahangir; Hossain, Sohrab

doi:10.1109/icirca48905.2020.9182954

Cited by 40 publications

(21 citation statements)

References 24 publications

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“…The results of the adjustments are stored to structure a knowledge and learning base, which serves as a reference in the investigation of fraudulent transactions (Pambudi et al, 2019). Thus, the storage module intends to store the data and send it to the detection system when necessary (Sarma et al, 2020).…”

Section: General Concept According To the Selected Articlesmentioning

confidence: 99%

“…Detection can be performed by analyzing the behavior of users when accessing financial services, tracking financial activities, and identifying transactions carried out (Dhaya & Ravi, 2020). Thus, the main elements of the detection algorithm are related to the raw data that were obtained, classified, and preprocessed (Sarma et al, 2020).…”

Section: General Concept According To the Selected Articlesmentioning

confidence: 99%

“…Chmielewski and Stąpor (2016) summarized the implementation of the Information Analysis of Financial and Economic Crime Ontology Toolkit concept for identifying financial fraud based on a set of ontologies to solve the problem at hand. Sarma et al (2020) explored the use of an algorithm based on an artificial neural network to establish a framework for detecting financial fraud. Mahootiha et al (2021) determined a pattern of action in cases of fraud on social networks, based on user behavior.…”

Section: Anti‐money Laundering and Financial Fraud Detectionmentioning

confidence: 99%

“…In this concept, researchers evaluated specific keywords within electronic transactions (Chang et al, 2007), characterized transactions based on user behavior (Gaganis, 2009; Hoogs et al, 2007; Rao & Kanchana, 2018), and used programs such as FraudJudger in payment platforms (Deng et al, 2020). In addition to behavior analysis, techniques based on theoretical graph approaches are also employed (Prakash et al, 2019), such as the Neo4j and Cypher query (Sarma et al, 2020). In contrast, numerical and statistical tests can be used to detect suspicious ML activities (Yang & Wei, 2010) using programs based on scan statistics (Liu & Zhang, 2010) and probability models (Jayasree & Balan, 2015).…”

Section: Anti‐money Laundering and Financial Fraud Detectionmentioning

confidence: 99%

See 3 more Smart Citations

Anti‐money laundering and financial fraud detection: A systematic literature review

Goecks

Korzenowski

Neto

et al. 2022

Intell Sys Acc Fin Mgmt

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Money laundering has affected the global economy for many years, and there are several methods of solving it presented in the literature. However, when tackling money laundering and financial fraud together there are few methods for solving them. Thus, this study aims to identify methods for anti‐money laundering (AML) and financial fraud detection (FFD). A systematic literature review was performed for analysis and research of the methods used, utilizing the SCOPUS and Web of Science databases. Of the 48 articles that aligned with the research theme, 20 used quantitative methods for AML and FFD solution, 13 were literature reviews, 7 used qualitative methods, and 8 used mixed methods. This study contributes by presenting a systematic literature review that fills two research gaps: lack of studies on AML and FFD, and the methods used to solve them. This will assist researchers in identifying gaps and related research.

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Section: General Concept According To the Selected Articlesmentioning

confidence: 99%

Section: General Concept According To the Selected Articlesmentioning

confidence: 99%

Section: Anti‐money Laundering and Financial Fraud Detectionmentioning

confidence: 99%

Section: Anti‐money Laundering and Financial Fraud Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Anti‐money laundering and financial fraud detection: A systematic literature review

Goecks

Korzenowski

Neto

et al. 2022

Intell Sys Acc Fin Mgmt

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“…the organization of vertices in clusters, can be detected and considered as fairly independent compartments of a graph (Fortunato, 2010). Detecting communities is of great importance in sociology, biology and computer science, which have been applied to real networks such as social networks (Bedi & Sharma, 2016), bank fraud (Sarma et al, 2020) and biological networks (Buhnerkempe et al, 2016; Tripathi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

habCluster: Identifying Geographical Boundary among Intraspecific Units Using Community Detection Algorithm in R

Zhang

Yang

et al. 2022

Preprint

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Conservation management for a species generally rests on intraspecific units, while identification of their geographic boundaries is necessary for the implementation. Intraspecific units can be discriminated using population genetic methods, yet an analytical approach is still lacking for detecting their geographic boundaries. Here, based on landscape connectivity, we present a geographical boundary delineation method, habCluster, using community detection algorithm. Community detection is an algorithm in graph theory used to identify clusters of highly connected nodes within a network. We assume that the habitat raster cells with better connections tend to form a continuous habitat patch than the others, thus making the range of an intraspecific unit. The method is tested on grey wolf (Canis lupus) habitat in Europe and on giant panda (Ailuropoda melanoleuca) habitat in China. The habitat suitability for grey wolves and giant pandas were evaluated using species distribution modeling. Each cell in the habitat suitability index (HSI) raster is treated as a node and directly connected with its eight neighbor cells. The edge weight between nodes is the distance between the center of them weighted by the average of their HSI values. We implement habCluster using R programming language with inline C++ code to speed up the computing. The geographical clusters detected were compared with the HSI maps for both species and with the nature reserves for giant panda. We found the boundaries of the clusters delineated using habCluster could serve as a good indicator of habitat patches, and they match generally well with nature reserves in the giant panda case. habCluster can provide spatial analysis basis for conservation management plans such as monitoring, translocation and reintroduction, as well as for population structure research.

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Using Relational Graph Convolutional Networks to Assign Fashion Communities to Users

Budhiraja¹,

Sukhwani²,

Aggarwal³

et al. 2022

Lecture Notes in Electrical Engineering

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Bank Fraud Detection using Community Detection Algorithm

Cited by 40 publications

References 24 publications

Anti‐money laundering and financial fraud detection: A systematic literature review

Anti‐money laundering and financial fraud detection: A systematic literature review

habCluster: Identifying Geographical Boundary among Intraspecific Units Using Community Detection Algorithm in R

Using Relational Graph Convolutional Networks to Assign Fashion Communities to Users

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