From idea to production.

On a platform delivering digital goods within seconds, a stolen card meant losing the product and paying a chargeback fee. Fraud could be reported up to three months after purchase. The existing hand-written rules engine failed to catch most fraudulent orders.

Score every order at checkout, before and after payment, on extremely imbalanced data whose true label can arrive up to 3 months later. Keep manual reviews within capacity without reducing conversion.

Five years of iteration in production: nearly 200 real-time features (rolling fraud rates, behavioral time windows, circular statistics); models that evolved from a random forest to an MLP, then to XGBoost with dual-model serving; automated retraining with quality gates, including a cap on manual-review volume before deployment; and model hot-swapping via pub/sub.

An online retailer of digital goods received part of its inventory as scanned sheets holding many activation keys. Staff cropped each key manually, taking several minutes per image.

Identify every key in supplier images with different rotations, noise, and surrounding text, without an existing dataset, then integrate the tool into the back office for nontechnical staff.

A semi-automatically annotated dataset of more than 20,000 keys; a custom Faster R-CNN detector later migrated to YOLOv5; post-processing calibrated to the expected number of keys; and a human-validation interface that feeds verified corrections back into the training data.