Semi-supervised Bayesian GANs with log-signatures for uncertainty-aware credit card fraud detection show consistent improvements over benchmarks on the BankSim simulator under varying label proportions.
Feature engineering strategies for credit card fraud detection,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Presents a three-component fusion AI agent for multi-vector fraud and AML detection in retail/corporate banking using LSTM, statistical, and graph modules on synthetic data, reporting F1 scores of 0.787 (transactions) and 0.867 (sessions).
citing papers explorer
-
Semi-Supervised Bayesian GANs with Log-Signatures for Uncertainty-Aware Credit Card Fraud Detection
Semi-supervised Bayesian GANs with log-signatures for uncertainty-aware credit card fraud detection show consistent improvements over benchmarks on the BankSim simulator under varying label proportions.
-
An AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts
Presents a three-component fusion AI agent for multi-vector fraud and AML detection in retail/corporate banking using LSTM, statistical, and graph modules on synthetic data, reporting F1 scores of 0.787 (transactions) and 0.867 (sessions).