Author(s): Mamadou MBAYE
This article presents an innovative theoretical model for banking customer profiling using Artificial Intelligence (AI), aiming to optimize customer relationship management, risk assessment and tailor-made service proposition. With the emergence of web 2.0 and especially web 3.0, the financial sector has experienced a real revolution thanks to AI and Machine Learning (ML) technologies. Among the most promising applications in the banking field, customer profiling stands out as a major subject for professionals and researchers. The proposed model combines AI algorithms and data analysis techniques to distill strategic customer insights. It integrates essential aspects such as the collection and preprocessing of historical transaction data, demographic information, and online behaviors. It aims to extract meaningful features such as spending habits, income levels and risk factors. It facilitates the segmentation of customers into distinct groups, based on their behaviors and financial characteristics, thus enabling a more refined risk assessment than that offered by traditional credit scoring methods. Additionally, it makes personalized product recommendations by analyzing customer preferences and behavior. The model also includes a fraud detection function through continuous monitoring of transactions, thus identifying abnormal or suspicious behavior. This proactive approach helps banks protect their customers' assets by also taking into account ethical considerations and compliance with privacy and data regulations, ensuring responsible use of AI. As the financial sector evolves, the integration of AI into customer profiling marks an important step towards a more customer-centric banking landscape driven by big data analytics. This model helps reduce human errors of judgment, credit risk and optimizes decision-making in the banking sector.