Financial forecasts influence hiring, spending, cash management, investment, capacity, and growth decisions. Yet the variables behind those forecasts rarely move independently. Revenue can shift with demand, pricing, customer behavior, sales performance, and market conditions, while costs respond to workforce, procurement, capacity,…
Predictive Modeling & Statistical Analytics
Models that forecast outcomes and quantify uncertainty
Predictive models are only as valuable as the accuracy, explainability, and operational integration of their outputs. We build statistical and ML-based predictive models that are production-grade — validated against holdout data, monitored for drift, and integrated into the workflows where their predictions drive real decisions.
- Supervised learning models: classification and regression for churn prediction, credit scoring, denial risk, and demand forecasting
- Time-series forecasting — ARIMA, Prophet, LSTM, and ensemble models for revenue, demand, inventory, and operational metric forecasting
- Survival and hazard modeling — time-to-event analysis for patient readmission, equipment failure, customer lifetime value, and churn timing
- Ensemble and gradient boosting — XGBoost, LightGBM, and CatBoost models for high-accuracy classification in fraud, risk, and clinical applications
- Statistical inference and experimentation — A/B testing frameworks, causal inference, and uplift modeling for treatment effect measurement
- Model explainability appropriate to the model and the decision it drives: feature importance, prediction-level explanations, and documentation that supports audit and regulatory review
