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LOCATION:Davos
DTSTART;TZID=Europe/Stockholm:20230626T112000
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UID:submissions.pasc-conference.org_PASC23_sess104_pos136@linklings.com
SUMMARY:P17 - Detecting Financial Fraud with Graph Neural Networks
DESCRIPTION:Poster\n\nJulien Schmidt, Dimosthenis Pasadakis, and Olaf Sche
 nk (Università della Svizzera italiana)\n\nDetecting financial fraud is a 
 challenging classification problem that entails the discovery of suspiciou
 s patterns in large-scale and time evolving data. Traditionally, financial
  institutions have been relying on rule-based methods to identify suspicio
 us accounts, with such approaches becoming ineffective as the volume of tr
 ansactions grows and criminal conduct gets more sophisticated. In this wor
 k, we capture in the form of directed graphs the interdependent nature of 
 monetary transactions, with their nodes representing the financial entitie
 s involved, and their edges describing details regarding the transactions.
  We consider this format in both static and dynamic graph structures which
  mimic a time evolving financial environment. Subsequently, deep learning 
 anomaly detection approaches are employed with the aim of separating fraud
 ulent and benign nodes using their historical data. We consider classifier
 s including Graph Convolutional Networks, Graph Attention Networks, and Lo
 ng-Short Term Memory Autoencoders and apply them on a wide range of artifi
 cial data that simulate the real-world behavior and complexity of monetary
  transactions. The accuracy and time-to-solution of our results highlight 
 the applicability of deep learning methods in problems encountered by the 
 financial industry.\n\nSession Chair: Elaine M. Raybourn (Sandia National 
 Laboratories)
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