Temporal Network Analytics for Fraud Detection in the Banking Sector

Hajdú, László; Krész, Miklós

doi:10.1007/978-3-030-55814-7_12

Cited by 11 publications

(9 citation statements)

References 16 publications

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“…Their detection model is based on the ground-truth information, thus unknown complex mixing strategies that may exist might not be detected. In [19], the authors propose an approach for detecting frauds using money trails in the temporal transactions graph. They integrate their approach with a real-time fraud detection system in a private bank.…”

Section: Related Workmentioning

confidence: 99%

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Understanding Money Trails of Suspicious Activities in a cryptocurrency-based Blockchain

Lal,

Agarwal,

Shukla

2021

Preprint

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The decentralization, redundancy, and pseudo-anonymity features have made permission-less public blockchain platforms attractive for adoption as technology platforms for cryptocurrencies. However, such adoption has enabled cybercriminals to exploit vulnerabilities in blockchain platforms and target the users through social engineering to carry out malicious activities. Most of the state-of-the-art techniques for detecting malicious actors depend on the transactional behavior of individual wallet addresses but do not analyze the money trails. We propose a heuristics-based approach that adds new features associated with money trails to analyze and find suspicious activities in cryptocurrency blockchains. Here, we focus only on the cyclic behavior and identify hidden patterns present in the temporal transactions graphs in a blockchain. We demonstrate our methods on the transaction data of the Ethereum blockchain. We find that malicious activities (such as Gambling, Phishing, and Money Laundering) have different cyclic patterns in Ethereum. We also identify two suspicious temporal cyclic path-based transfers in Ethereum. Our techniques may apply to other cryptocurrency blockchains with appropriate modifications adapted to the nature of the crypto-currency under investigation.

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Section: Related Workmentioning

confidence: 99%

“…This approach was able to detect 2-4 new illicit activities every month since 2017. The approach in [19] also allowed banks to set new preferences (for example, the limited time between transactions or maximum percentage of money loss is allowed).…”

Section: Related Workmentioning

confidence: 99%

Understanding Money Trails of Suspicious Activities in a cryptocurrency-based Blockchain

Lal,

Agarwal,

Shukla

2021

Preprint

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“…In the paper "Temporal network analytics for fraud detection in the banking sector" [12], the authors present a new methodology in temporal networks for fraud detection in the banking sector. While, standard approaches of fraudulence monitoring mainly have the focus on the individual client data, the authors' approach concentrate on the hidden data produced by the network of a transaction database.…”

Section: Selected Papersmentioning

confidence: 99%

Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium

Bellatreche

Bentayeb

Bieliková

et al. 2020

ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

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“…Their experiments demonstrate that detecting cycles is more effective in finding potential fraudulent groups than using other methods, such as leading eigenvector. [26] applies the cycle detection problem to a fraud detection system of financial institutions and demonstrates its effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Towards Real-Time Counting Shortest Cycles on Dynamic Graphs: A Hub Labeling Approach

Feng¹,

Peng²,

Zhang³

et al. 2022

2022 IEEE 38th International Conference on Data Engineering (ICDE)

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With the ever-increasing prevalence of graph data in a wide spectrum of applications, it becomes essential to analyze structural trends in dynamic graphs on a continual basis. The shortest cycle is a fundamental pattern in graph analytics. In this paper, we investigate the problem of shortest cycle counting for a given vertex in dynamic graphs in light of its applicability to problems such as fraud detection. To address such queries efficiently, we propose a 2-hop labeling based algorithm called Counting Shortest Cycle (CSC for short). Additionally, techniques for dynamically updating the CSC index are explored. Comprehensive experiments are conducted to demonstrate the efficiency and effectiveness of our method. In particular, CSC enables query evaluation in a few hundreds of microseconds for graphs with millions of edges, and improves query efficiency by two orders of magnitude when compared to the baseline solutions. Also, the update algorithm could efficiently cope with edge insertions (deletions).

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Temporal Network Analytics for Fraud Detection in the Banking Sector

Cited by 11 publications

References 16 publications

Understanding Money Trails of Suspicious Activities in a cryptocurrency-based Blockchain

Understanding Money Trails of Suspicious Activities in a cryptocurrency-based Blockchain

Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium

Towards Real-Time Counting Shortest Cycles on Dynamic Graphs: A Hub Labeling Approach

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