Risk detection in cryptocurrency markets: meeting the needs of traditional finance

Aguilar, Iván; Jones, Rebecca; Lovicu, Gian-Piero

Risk detection in cryptocurrency markets: meeting the needs of traditional finance

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Citation

Aguilar, I, Jones, R, Lovicu, G. Risk detection in cryptocurrency markets: meeting the needs of traditional finance. 2022. handle: http://hdl.handle.net/10230/56570

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Abstract

Financial institutions are beginning to integrate cryptocurrencies into their payment systems but must ensure to comply with anti-money laundering regulations to avoid facilitating transactions linked to criminal activities. We propose a cryptocurrency risk detection model that could be used by these institutions. It is novel in two ways: firstly, it prioritises a high recall, and secondly, organises the transaction data in a different 'address-level' manner. We test different Graph Neural Network (GNN) models and find that the Graph Attention Network using our address-level data achieves a recall of 83%, an improvement on results achieved in previous literature.
Les entitats financeres comencen a integrar criptomonedes als seus sistemes de pagament, però han de garantir el compliment de la normativa contra el blanqueig de capitals per evitar facilitar les transaccions vinculades a activitats delictives. Proposem un model de detecció de risc de criptomoneda que podrien utilitzar aquestes institucions. És nou de dues maneres: en primer lloc, prioritza un record elevat i també organitza les dades de la transacció d'una manera diferent de "nivell d'adreça". Provem diferents models de xarxa neuronal de gràfics (GNN) i trobem que la xarxa d'atenció gràfica utilitzant les nostres dades a nivell d'adreça aconsegueix un record del 83%, una millora dels resultats assolits en la literatura anterior.