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Found 9 papers, 2 papers with code
Adversarial training for tabular data with attack propagation
Adversarial attacks are a major concern in security-centered applications, where malicious actors continuously try to mislead Machine Learning (ML) models into wrongly classifying fraudulent activity as legitimate, whereas system maintainers try to stop them.
The GANfather: Controllable generation of malicious activity to improve defence systems
In the former, our method moves cumulative amounts close to 350 thousand dollars through a network of accounts without being detected by an existing system.
Lightweight Automated Feature Monitoring for Data Streams
We propose a flexible system, Feature Monitoring (FM), that detects data drifts in such data sets, with a small and constant memory footprint and a small computational cost in streaming applications.
Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs
Money laundering is a global problem that concerns legitimizing proceeds from serious felonies (1. 7-4 trillion euros annually) such as drug dealing, human trafficking, or corruption.
Active learning for imbalanced data under cold start
Modern systems that rely on Machine Learning (ML) for predictive modelling, may suffer from the cold-start problem: supervised models work well but, initially, there are no labels, which are costly or slow to obtain.
GuiltyWalker: Distance to illicit nodes in the Bitcoin network
Money laundering is a global phenomenon with wide-reaching social and economic consequences.
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity
First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset.
Interleaved Sequence RNNs for Fraud Detection
Payment card fraud causes multibillion dollar losses for banks and merchants worldwide, often fueling complex criminal activities.
ARMS: Automated rules management system for fraud detection
We propose ARMS, an automated rules management system that evaluates the contribution of individual rules and optimizes the set of active rules using heuristic search and a user-defined loss-function.
