Optimizing Preferential Rate in Retail Lending with Causal Inference and Domain Adaptation

Abstract

In retail lending, offering preferential interest rates is a core marketing instrument for balancing customer acquisition with portfolio profitability. Accurately predicting the effect of interest-rate discounts for each customer is pivotal for optimizing the discount strategy: offering overly generous discounts erodes margins, while insufficient discounts drive price-sensitive customers to defect. Off‑the‑shelf machine learning uplift models rarely respect the complex operational constraints of financial business, such as tiered rate grids, regulatory guard‑rails, and marketing budget ceilings. We propose an integrated system that fuses causal inference and domain adaptation to produce constraint‑aware, customer‑specific discount recommendations. To further enhance practitioner adoption, a large language model layer translates model outputs into actionable narratives. Developed in Hyundai Capital Services, the system boosted transaction volume by 13%, demonstrating both technical soundness and material business impact.