CoDetect: Financial Fraud Detection With Anomaly Feature Detection

Anomaly detection tries to find out the data that disobeys the rule of majority data or expected patterns. The traditional hierarchical clustering algorithms have been adopted to detect anomaly, but have the disadvantages of low effectiveness and unstability. So we propose an improved agglomerative hierarchical clustering method for anomaly detection. It dynamically adjusts the optimum clustering number according to the self-defined criterion to save the trouble of manually picking clustering number, and determines the optimum clustering distance mode according to cophenetic correlation coefficient to reduce the procedure of manually testing the suitable distance mode in each iteration. The performances of proposed method are verified on tensile test, HTRU2 and credit card dataset. Compared with the traditional methods, our method possesses the most comprehensive performance (the highest F-measure with less iterations), which shows effectiveness of anomaly detection. And compared with the traditional methods (single, complete, average, and centroid mode), our method achieves the best performance on tensile test and HTRU2 dataset, showing stronger generalization. Compared with other methods (Decision + Gradient Boosted Tree, Decision Trees + Decision Stump, etc) on credit card dataset, our method obtains similar accuracy, and ranks in the top level in the aspect of sensitivity.

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“…Secondly, when the clustering number is n, calculate the centroid c ni (1 ≤ i ≤ k) of samples in each cluster by formula (4).…”

Section: F I G U R E 3 Process Of Selecting the Optimum Clustering Numentioning

confidence: 99%

An improved agglomerative hierarchical clustering anomaly detection method for scientific data

Shi

Zhao

Zhong

et al. 2020

Concurrency and Computation

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“…To identify the fraud, the framework provides a way of sparse matrix. By the help of CoDetect framework, not only the financial fraud supervision can be detected and also by suspicions method it can be able to trace the original fraud [12]. In today's world the fraud detection activity for national economics have become an important task and also in the international economics.…”

Section: Devika S P Nisarga K S Gagana P Rao Chandini S B Rajkumar Nmentioning

confidence: 99%

“…A fraud happens when a unknown user uses somebody's credit card for personal usage and even the issuing banks are unaware of the card being used [11]. The fraudulent of transaction reveals the interactions between entities and anomaly detection on features that detect details of fraud activities [12]. The fraudulent quickly identifies the thresholds and take advantages and exploit the tools which remain static [13].…”

Section: Introductionmentioning

confidence: 99%

A Research on Credit Card Fraudulent Detection System

Devika¹,

Nisarga²,

Rao³

et al. 2019

IJRTE

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Nowadays credit card is more popular among the private and public employees. By using the credit card, the users purchase the consumable durable products in online, also transferring the amount from one account to other. The fraudster is detecting the details of the behavior user transaction and doing the illegal activities with the card by phishing, Trojan virus, etc. The fraudulent may threaten the users on their sensitive information. In this paper, we have discussed various methods of detecting and controlling the fraudulent activities. This will be helpful to improve the security for card transaction in future. I.

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“…Graph mining approaches have been used to detect suspicious sets of transactions through graph isomorphism [26], detecting fraudulent behaviour using graph based anomaly detection (GBAD) [27,28], and role based approaches have been used to detect terrorist groups [29]. These approaches are not applicable in the stated context due to the uncertainty and incompleteness present in the data, not to mention the scale of the data, which is significant enough to preclude the use of such supervised learning approaches.…”

Section: Related Workmentioning

confidence: 99%

The detection of criminal groups in real-world fused data: using the graph-mining algorithm “GraphExtract”

Robinson

Scogings

2018

Secur Inform

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Law enforcement and intelligence agencies generally have access to a number of rich data sources, both structured and unstructured, and with the advent of high performing entity resolution it is now possible to fuse multiple heterogeneous datasets into an explicit generic data representation. But once this is achieved how should agencies go about attempting to exploit this data by proactively identifying criminal events and the actors responsible? The authors will outline an effective generic method that; computationally extracts minimally overlapping contextual subgraphs, then uses these subgraphs as the basis to construct a mesoscopic graph based on the intersections between the subgraphs, enabling knowledge discovery from these data representations for the purpose of maximally disrupting terrorism, organised crime and the broader criminal network.

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CoDetect: Financial Fraud Detection With Anomaly Feature Detection

Cited by 94 publications

References 38 publications

An improved agglomerative hierarchical clustering anomaly detection method for scientific data

An improved agglomerative hierarchical clustering anomaly detection method for scientific data

A Research on Credit Card Fraudulent Detection System

The detection of criminal groups in real-world fused data: using the graph-mining algorithm “GraphExtract”

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