Temporal Debiasing using Adversarial Loss based GNN architecture for Crypto Fraud Detection

The tremendous rise of cryptocurrency in the payment domain has unlocked huge opportunities but also raised numerous challenges in parallel involving cybercriminal activities like money laundering, terrorist financing, illegal and risky services, etc, owing to its anonymous and decentralized setup. The demand for building a more transparent cryptocurrency network, resilient to such activities, has risen extensively as more financial institutions look to incorporate it into their network. While a plethora of traditional machine learning and graph based deep learning techniques have been developed to detect illicit activities in a cryptocurrency transaction network, the challenge of generalization and robust model performance on future timesteps still exists. In this paper, we show that the model learned on transactional feature set provided in dataset (Elliptic Dataset) carry a temporal bias, i.e. they are highly dependent on the timesteps they occur. Deploying temporally biased models limits their performance on future timesteps. To address this, we propose a temporal debiasing technique using GNN based architecture that ensures generalization by adversarially learning between fraud(1) classification and temporal classification. The adversarial loss constructed optimizes the embeddings to ensure they 1.) perform well on fraud classification task 2.) does not contain temporal bias. The proposed architecture capture the underlying fraud patterns that remain consistent over time. We evaluate the performance of our proposed architecture on the Elliptic dataset and compare the performance with existing machine learning and graph-based architectures.

Automated summary

The rise of cryptocurrency has presented both opportunities and challenges, with a growing demand for transparent networks resilient to illegal activities. Existing technologies still struggle with performance in future time steps.