Neuroadam Labs designs and deploys bespoke ML infrastructure for fintechs and financial institutions — graph neural networks, gradient-boosted ensembles and transformer sequence models running at sub-100ms inference to lift authorization rates, intercept fraud in real time, and make compliance explainable and audit-ready.
Reinforcement-learning routing policies and causal uplift models that recover declines — optimizing BIN-level retry cascades, network tokenization and issuer-response signals against a calibrated revenue objective.
Graph neural networks and gradient-boosted ensembles scoring transactions in sub-100ms — trained on adversarial examples, isotonic-calibrated, and tuned along the precision-recall frontier to your chargeback and loss tolerances.
Entity-resolution, embedding-based link analysis and sequence models for AML, KYC and transaction monitoring — fully SHAP-explainable, drift-monitored, and documented to withstand regulatory and model-risk review.
Every engagement starts from your data distribution, your risk appetite and your stack. We own the
full lifecycle — feature engineering and real-time feature stores, Bayesian hyperparameter search,
training and evaluation, then deployment behind streaming inference with continuous
drift detection, automated retraining and full observability. The deliverable
is a system that holds up under production load and regulatory scrutiny — not a notebook.
Tell us about the problem — authorization lift, a fraud model, a transaction-monitoring system,
or something adjacent. Messages route to contact@neuroadam.com and we reply directly.