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Graphomaly - software package for anomaly detection in graphs modeling financial transactions

Project PN-III-P2-2.1-PED-2019-3248, PED 2019

Description and objectives

Team

Documents

Journal papers

Conference papers

Software

Description and objectives

The Graphomaly project aims to create a Python software package for anomaly detection in graphs that model financial transactions, with the purpose of discovering fraudulent behavior like money laundering, illegal networks, tax evasion, scams, etc. Such a toolbox is necessary in banks, where fraud detection departments still use mostly human experts.

We aim to propose and test specific algorithms for financial graphs analysis and apply anomaly detection tools, among which those based on dictionary learning will have a prominent place, on the resulting features and characteristic information.

The implemented methods will be able to process large graphs. Online and distributed forms of the algorithms will be derived, such that reaction time is decreased and thus frauds can be discovered in their incipient stages.

This work is funded by Romanian Ministry of Education and Research, CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED-2019-3248, within PNCDI III.

Team
The three partners involved in this project and the team members are
  • University Politehnica of Bucharest: prof. Bogdan Dumitrescu, prof. Florin Stoican, drd. Denis Ilie-Ablachim
  • University of Bucharest: conf. Paul Irofti, conf. Andrei Pătraşcu, drd. Andra Băltoiu, prof. Marius Popescu
  • Tremend Software Consulting SRL: Ioana Rădulescu, Ştefania Budulan, Alexandra Bodîrlău
External partner: Libra Internet Bank, providing transaction data.
Documents
Journal papers
  • A. Pătraşcu, P.Irofti, "Computational complexity of Inexact Proximal Point Algorithm for Convex Optimization under Holderian Growth", submitted to Journal of Machine Learning Research, Aug. 2021. Files: submitted version
Conference papers
  • B.Dumitrescu, D.Ilie-Ablachim, "Classification with Incoherent Kernel Dictionary Learning", Int. Conf. Control Systems and Computer Science, Bucharest, May 2021. Files: final version
  • F.I.Miertoiu, B.Dumitrescu, "Shape Parameter and Sparse Representation Recovery under Generalized Gaussian Noise", EUSIPCO, Dublin, Ireland, Aug. 2021. Files: final version
  • C.Rusu, P.Irofti, "Efficient and Parallel Separable Dictionary Learning", IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS), Beijing, China, Dec. 2021. Files: final version
  • P.Irofti, L.Romero-Ben, F.Stoican, V.Puig, "Data-driven Leak Localization in Water Distribution Networks via Dictionary Learning and Graph-based Interpolation", submitted to 2022 American Control Conference (ACC). Files: submitted version
Software
  • dictlearn - a dictionary learning library in Python: sources, docs