Where does DL beat classic quant?
DL wins out in fast pricing/risk via neural surrogates, short-horizon forecasting from order-book data (LSTM/GRU), and cost-aware hedging with reinforcement learning.
How much data is needed—and can synthetic data help?
Use as much clean, labeled history as possible. Fill gaps with VAEs/GANs for scenario expansion and privacy, then validate on held-out real data.
Can Greeks and risk from neural pricers be trusted?
Yes, if you use differential training (prices and sensitivities), enforce no-arbitrage/monotonicity, and monitor Greek drift in production.
How can we meet latency constraints in production?
Train offline; serve compact models on GPUs/CPUs (or FPGAs for ultra-low latency); cache results; and deploy as drop-in surrogates alongside current pricers.
What satisfies model risk and regulators?
Model risk teams and regulators are satisfied when you ship models with built-in explainability (feature attributions, sensitivity tests), documented data lineage, active champion–challenger (challenger models) setups, proven stability across market regimes, and explicit, enforced usage limits.
Does RL work live?
It can, when trained with realistic costs/liquidity and run with guardrails (position limits, kill-switches, stress triggers) plus continuous post-trade monitoring.
