FROM FEATURES TO FINANCIAL PERSONAS: MAPPING FEATURE TRANSFORMATION EFFICACY TO CUSTOMER ARCHETYPES IN BEHAVIORAL BANKING DATA

Current research in customer behavior prediction within retail banking heavily benchmarks the aggregate performance of feature engineering and machine learning pipelines.While these studies demonstrate that techniques like logarithmic scaling, polynomial expansion, and interaction term creation improve overall classification accuracy for metrics such as customer activity status, they provide scant insight into the heterogeneous effects these transformations have across a bank's diverse customer base.This study posits that the efficacy of a feature transformation is not universal but is intrinsically linked to the underlying financial behavior pattern, or 'persona,' of the customer.To address this critical gap, we conduct a two-stage analytical process on a real-world dataset of 30,000 customers from a major European bank.First, we employ a combination of kmeans clustering and Gaussian Mixture Models on raw transactional and demographic features to derive five distinct, interpretable customer archetypes:The Digital Nomad (high-frequency, low-value digital transactions), The Traditional Accumulator (low-frequency, high-value branch-based savings), The Credit-Reliant (revolving credit users), The Multi-Product Holder (diversified https://ijcserd.com