Scaling Micro-Offers With Hybrid AI

This work proposes a pipeline for responsible investment offer personalization in Brazilian Open Finance, integrating data governance, segmentation (K-Means), propensity modeling (Random Forest) and an ethical filtering layer via LLM-based digital twins. Applied to 427,929 Caixa Econômica Federal customers, the model achieved an AUC-ROC of 0.57. Digital twin validation reduced the high-propensity pool from 213,764 to 6,543 eligibles, applying suitability rules and generating auditable justifications to mitigate overcontact. Distributive analysis indicated a concentration of offers among higher-income profiles. In a pilot within the My Wallet app, the approach yielded a 21.0% positive intention rate, surpassing the 3–5% generic average. Results suggest that combining Open Finance, ML and GenAI enhances assertiveness and algorithmic governance, highlighting the need for formal fairness metrics and large-scale causal experiments. Talk By: Andre Victor Ruiz Pedroso, Data Scientist, Caixa Economica Federal ; Jessyca Oliveira, Sr. Solutions Engineer, Databricks ; Connect with us: Website: https://databricks.com X: / databricks LinkedIn: / databricks Instagram: / databricksinc Facebook: / databricksinc