Title: ACLEGR-TADD: Adaptive Continual Learning for Financial Fraud Detection under Extreme Class Imbalance
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: continual learning, financial fraud detection, extreme class imbalance, concept drift, temporal attention, wavelet analysis, catastrophic forgetting, differential privacy

Abstract: Financial fraud detection systems face catastrophic performance degradation under adversarial concept drift and extreme class imbalance, where fraud comprises less than 0.2% of transactions. Existing continual learning methods fail as they assume balanced classes and static distributions. We propose ACLEGR-TADD, a novel framework that integrates Temporal Attention-based Drift Detection (TADD) with multi-resolution wavelet analysis, achieving a 4-fold reduction in detection delay (from 4.8h to 1.2h). Our method incorporates a Fraud-Aware Variational Memory Network (FA-VMN) that leverages class-specific variance disparities and Information-Theoretic Adaptive Consolidation (ITAC) using PAC-Bayes bounds. We provide the first catastrophic forgetting bound under extreme imbalance, proving that forgetting scales with the square root of the fraud rate over sample size. Experiments on five datasets comprising over 10 million transactions demonstrate that ACLEGR-TADD achieves 94.7% PR-AUC with sub-10ms CPU inference latency, significantly outperforming DER++ (74.7%) and FT-Transformer (78.1%). The framework satisfies differential privacy with formal guarantees while reducing false positives by 64% in production deployment.

TL;DR: We introduce ACLEGR-TADD, a continual learning framework that combines attention-based and wavelet drift detection to achieve 94.7% PR-AUC on financial fraud detection despite extreme class imbalance (0.2% fraud rate) and adversarial concept drift.