Financial Products; Recommendation Systems; Fintech; Classification.

Recommendation systems play a crucial role across various sectors, including finance, by providing personalized suggestions to clients based on their histories and preferences. In the financial and credit industries, such systems have the potential to revolutionize how institutions engage with clients, particularly through personalized recommendations for financial products like investments, insurance, and loans. This study analyzed 208,570 customer records from three types of financial insurance services to develop a solution capable of supporting marketing strategies for offering these services to the clients of a Fintech. Clustering with K-Means, tested with 2 to 6 clusters, revealed significant customer segmentation patterns. While the two-cluster configuration achieved the highest Silhouette Score (0.4169), the four-cluster approach provided more informative segmentation for strategic purposes. For predictive modeling, after initial tests and random hyperparameter search with 5-fold and 10-fold cross-validation with different models, XGBoost and LightGBM both reported 82% recall. LightGBM was selected for final evaluation due to its lower computational cost and comparable performance to XGBoost in this context. When applied to the validation set, which had a significantly different insurance distribution from the training data due to covariate shift, the model's performance dropped significantly to 43.1% recall. The model performed best with Insurance C (63.7% recall) but struggled with Insurances A (45.8% recall) and B (2.6% recall). When trained on 80% of the combined dataset and validated on the remaining 20%, LightGBM showed substantial improvements in accuracy and F1-score metrics for Insurances A and C, achieving recalls of 83.33% and 81.66%, respectively. Although the model's performance with combined data was significantly better, covariate shifts remain a critical challenge in modeling a solution for the purposes of this study.