AI-Based Customer Behavior Prediction in Banking and Insurance: An Applied Study

Author: Arun Kumar Gharami

Issue: Spring Issue, 2026

Abstract

Financial institutions can operate under capacity and legal limits by using customer behavior prediction to detect anomalous activity, reduce attrition, and personalize services. Using transactional, relational, and demographic characteristics from a de-identified banking and insurance dataset with 120,000 customers observed over a 24-month period, this study creates and assesses an applied machine-learning framework that forecasts four customer outcomes: product uptake, churn risk, claim propensity, and fraud risk. Temporal, behavioral, and engagement indicators such as tenure, product mix, customer-provider network features, and recency-frequency-monetary (RFM) measurements were generated through feature engineering. Logistic Regression, Random Forest, XGBoost, and a stacked ensemble with a logistic meta-learner trained on out-of-fold predictions are among the models that are compared. MAUC, PR-AUC, precision, recall, F1, Brier score, and calibration curves were used to assess model performance on a temporally separated holdout set. The stacked ensemble produced the best overall performance (average AUC ≈ 0.91 and average PR-AUC ≈ 0.64 across tasks) and well-calibrated probabilities (average Brier score ≈ 0.07). Predictions at the cohort and individual levels were interpreted using SHAP explanations, which showed that relative monetary activity, tenure, and recent engagement frequency were consistently the best predictors for all four outcomes. Targeted interventions based on estimated probabilities may boost cross-sell conversion by around 18% and lower churn by about 12%, according to a deployment simulation with basic cost assumptions, while enabling fraud and claims teams to reduce manual review volumes by roughly 30-40% at recall levels over 65% and precision levels above 60%. In order to enhance client outcomes and operational efficiency while adhering to explainable AI and governance standards, the study presents a workable, comprehensible pipeline that financial institutions can incorporate into decision workflows.