A solution architecture for financial institutions to handle illegal activities: a neural networks approach

Vikram, A.; Chennuru, S.; Rao, H. Raghav; Upadhyaya, Shuchita

doi:10.1109/hicss.2004.1265433

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…Its use of nearest‐neighbor matching and inductive retrieval also differs from ordinary expert systems. In another research, neural networks are used to correlate information from a variety of technology and database sources for financial institutions to identify suspicious account activity and handle illegitimate behavior (Vikram et al , 2004).…”

Section: Rare Transactional Pattern Recognitionmentioning

confidence: 99%

“…transaction on or access to an account) are related with predicted variables (e.g. risk of fraud or degree of unusualness on the account) (Vikram et al , 2004). This research is applicable for internal fraud, but its effectiveness might be weakened by its variable‐oriented rather than history‐oriented perspective of each transaction.…”

Section: Rare Transactional Pattern Recognitionmentioning

confidence: 99%

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A framework for data mining‐based anti‐money laundering research

Gao

2007

Journal of Money Laundering Control

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PurposeThe purpose of this paper is to propose a framework for data mining (DM)‐based anti‐money laundering (AML) research.Design/methodology/approachFirst, suspicion data are prepared by using DM techniques. Also, DM methods are compared with traditional investigation techniques. Next, rare transactional patterns are further categorized as unusual/abnormal/anomalous and suspicious patterns whose recognition also includes fraud/outlier detection. Then, in summarizing the reporting of money laundering (ML) crimes, an analysis is made on ML network generation, which involves link analysis, community generation, and network destabilization. Future research directions are derived from a review of literature.FindingsThe key of the framework lies in ML network analysis involving link analysis, community generation, and network destabilization.Originality/valueThe paper offers insights into DM in the context of AML.

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Section: Rare Transactional Pattern Recognitionmentioning

confidence: 99%

Section: Rare Transactional Pattern Recognitionmentioning

confidence: 99%

A framework for data mining‐based anti‐money laundering research

Gao

2007

Journal of Money Laundering Control

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“…They concluded that the banking industry's primary requirements are fraud detection and prevention and that data mining techniques can help reduce fraud cases. In addition, the work in [50] proposed the use of NN to correlate information from a variety of technological sources and databases in order to identify suspicious account activity. The work in [52] applied data mining algorithms, such as a SVM and ANNs, to detect financial fraud.…”

Section: Card Transactions From An Indonesian Bankmentioning

confidence: 99%

“…[45,46,49,56,57,60,63,67,69] obtained the highest score of 2.5, which represents 83.33% of the maximum score that a preliminary study could obtain; on the other hand, Refs. [38,39,41,44,48,[50][51][52][53]55,59,65] obtained a score of 2, that represents 66.67% of the maximum score. Refs.…”

Section: Quality Assessmentmentioning

confidence: 99%

Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

2021

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Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within financial institutions and is a matter of general interest. The problem is particularly complex, since perpetrators of fraud could belong to any position, from top managers to payroll employees. Fraud detection has traditionally been performed by auditors, who mainly employ manual techniques. These could take too long to process fraud-related evidence. Data mining, machine learning, and, as of recently, deep learning strategies are being used to automate this type of processing. Many related techniques have been developed to analyze, detect, and prevent fraud-related behavior, with the fraud triangle associated with the classic auditing model being one of the most important of these. This work aims to review current work related to fraud detection that uses the fraud triangle in addition to machine learning and deep learning techniques. We used the Kitchenham methodology to analyze the research works related to fraud detection from the last decade. This review provides evidence that fraud is an area of active investigation. Several works related to fraud detection using machine learning techniques were identified without the evidence that they incorporated the fraud triangle as a method for more efficient analysis.

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