Wenxi Sun

The deployment of autonomous AI agents in derivatives markets has widened a practical gap between static model calibration and realized hedging outcomes. We introduce two reinforcement learning frameworks, a novel Replication Learning of Option Pricing (RLOP) approach and an adaptive extension of Q-learner in Black-Scholes (QLBS), that prioritize shortfall probability and align learning objectives with downside sensitive hedging. Using listed SPY and XOP options, we evaluate models using realized path delta hedging outcome distributions, shortfall probability, and tail risk measures such as Expected Shortfall. Empirically, RLOP reduces shortfall frequency in most slices and shows the clearest tail-risk improvements in stress, while implied volatility fit often favors parametric models yet poorly predicts after-cost hedging performance. This friction-aware RL framework supports a practical approach to autonomous derivatives risk management as AI-augmented trading systems scale.

We extend the Q-learner in Black-Scholes (QLBS) framework by incorporating risk aversion and trading costs, and propose a novel Replication Learning of Option Pricing (RLOP) approach. Both methods are fully compatible with standard reinforcement learning algorithms and operate under market frictions. Using SPY and XOP option data, we evaluate performance along static and dynamic dimensions. Adaptive-QLBS achieves higher static pricing accuracy in implied volatility space, while RLOP delivers superior dynamic hedging performance by reducing shortfall probability. These results highlight the importance of evaluating option pricing models beyond static fit, emphasizing realized hedging outcomes.

Fraud in financial services—especially account opening fraud—poses major operational and reputational risks. Static rules struggle to adapt to evolving tactics, missing novel patterns and generating excessive false positives. Machine learning promises adaptive detection, but deployment faces severe class imbalance: in the NeurIPS 2022 BAF Base benchmark used here, fraud prevalence is 1.10%. Standard metrics (accuracy, f1_weighted) can look strong while doing little for the minority class. We compare logistic regression, SVM (RBF), Random Forest, LightGBM, and a GRU model on N=1,000,000 accounts under a unified preprocessing pipeline. All models are trained to minimize their loss function, while configurations are selected on a stratified development set using validation 1_weighted. For the four classical models, class weighting in the loss (class_weight in {None, 'balanced'}) is treated as a hyperparameter and tuned. Similarly, the GRU is trained with a fixed class-weighted cross-entropy loss that up-weights fraud cases. This ensures that both model families leverage weighted training objectives, while their final hyperparameters are consistently selected by the f1_weighted metric. Despite similar AUCs and aligned feature importance across families, the classical models converge to high-precision, low-recall solutions (1-6% fraud recall), whereas the GRU recovers 78% recall at 5% precision (AUC = 0.8800). Under extreme imbalance, objective choice and operating point matter at least as much as architecture.

Fraud detection in financial services is challenged by severe class imbalance, where fraudulent events are rare. This study provides a rigorous comparison of classical machine learning models—Logistic Regression, SVM, Random Forest, and LightGBM—and a Gated Recurrent Unit (GRU) deep learning model on a large-scale, imbalanced bank account fraud dataset. Our findings reveal a dramatic performance divide. All classical models, including the widely-used tree-based ensembles, proved ineffective at the primary detection task, with fraud recall scores below 7%.In stark contrast, the GRU network successfully identified the vast majority of fraudulent cases, achieving over 78% recall on the minority fraud class, albeit at the cost of very low precision. The results demonstrate that the GRU offers a viable, high-recall solution where classical models fail. This highlights a critical strategic choice for financial institutions: adopt a high-recall/high-alert detection framework or use conventional models that allow most fraud to go undetected. The approach is broadly applicable across financial domains—including banking and insurance—where rare-event detection is critical.