Move beyond static pricing rules. Deploy causal ML pipelines that estimate dose-response curves, optimize interest rates per segment, and forecast treasury flows. Pipelines are defensible, interpretable, and scalable across countries. Delivered as a Horizon module (treasury and pricing forecasting) plus an engineering sprint for the causal pipeline.
Pricing decisions based on rigid grids and historical rules, not on actual customer response to rate changes.
Predictive models estimate correlation, not causation. You don't know if lowering rates actually causes higher conversion or if it's confounded.
True randomized experiments are limited. You need quasi-experimental methods that extract causal signal from observational data.
Different countries have different regulatory constraints, customer behaviors, and competitive landscapes. One-size-fits-all pricing fails.
Dose-response estimation per segment using S-learners, augmented IPTW, and regression adjustment with overlap diagnostics
Compute optimal rates per segment by maximizing expected profit subject to business and regulatory constraints
Model fixed/variable costs, collection/payment cycles, and seasonal peaks to predict cash positions and liquidity risks
Customer- or segment-level rate optimization
Adjusts for confounding, avoids biased elasticity estimates
Interpretable with overlap diagnostics and sensitivity checks
Pipeline applied with local adjustments per market