Tech-Enhanced Risk Assessment For Anti-Money Laundering: Insights From USA Regional Banks

Abstract

This study looks at how technology advancements have affected regional banks' attempts to combat money laundering (AML) in the United States. The study obtained data from a representative sample of 185 respondents by using standardized questionnaires that were filled out by bank employees who were actively involved in AML operations. The investigation, which made use of a modified structural equation model, concentrated on four main concepts: the application of machine learning (ML) techniques, integration of Financial Intelligence Units (FIUs), Money Laundering Prevention (MLP), Risk Assessment (RA), and Suspicious Transaction Detection (STD). The study's conclusions shed light on how incorporating cutting-edge technologies might increase AML programs' efficacy and enhance risk assessment and preventative actions in local banking environments.