User-Centric Approaches to Fraud Detection: Incorporating Behavioral Analytics in Azerbaijan’s Banking Systems

EasyChair Preprint 14362

Abstract

Fraud detection in banking systems has traditionally relied on rule-based systems and heuristic methods. However, the integration of user-centric approaches, particularly through behavioral analytics, has emerged as a transformative strategy. This article explores how incorporating behavioral analytics into fraud detection systems can enhance security and efficiency in Azerbaijan's banking sector. By focusing on user behavior patterns, financial institutions can create more nuanced and effective fraud detection systems. This paper examines the current state of fraud detection in Azerbaijan, the benefits of behavioral analytics, and practical considerations for implementing these approaches within the regulatory and technological landscape of the country.

Keyphrases: Azerbaijan, Banking Systems, Digital transactions, Financial Crime, Regulatory Compliance, behavioral analytics, data privacy, fraud detection, machine learning, user-centric approaches

@booklet{EasyChair:14362,
  author    = {Adeoye Ibrahim},
  title     = {User-Centric Approaches to Fraud Detection: Incorporating Behavioral Analytics in Azerbaijan’s Banking Systems},
  howpublished = {EasyChair Preprint 14362},
  year      = {EasyChair, 2024}}