Alessandro Carlino

Progetto di ricerca
Data-Driven Approaches for The Analysis and Optimization of Banking Operations: Customer Satisfaction and Fraud Detection

This study addresses two critical aspects within the banking industry: customer satisfaction and fraud detection. By leveraging big data samples, the aim is to analyze and optimize these processes through the lens of data-driven approaches. Evaluating customer satisfaction amidst sales propositions, particularly through telemarketing in the credit industry, remains a challenge due to potential impacts on customer relationships. Leveraging an eight-year proprietary dataset from an Italian credit institution, this research identifies factors affecting customer satisfaction during ongoing client-bank relationships without compromising sales rates.

At the same time, the study delves into fraud detection within banking, specifically emphasizing targeted loan transactions—a niche area with limited academic research. Utilizing a methodical approach, it conducts a comprehensive analysis of banking fraud before narrowing its focus to targeted loans. Employing machine learning algorithms and clustering techniques on a transaction dataset containing five years of data, this research aims to fill the gap in academia by providing practical tools for detecting fraud within this underexplored domain.