Data Visualization in Financial Crime Detection: Applications in Credit Card Fraud

The COVID-19 pandemic has somewhat limited people’s mobility and made it difficult to buy goods and services offline, leading to a culture dependent on online services. One of the main problems with using credit cards is fraud, which is a major challenge in online shopping. This research addresses the transformative potential of data visualization techniques, particularly through the lens of combating financial crime, including credit card fraud and money laundering. The abstract highlights the complex landscape of visualizing financial data to uncover patterns and anomalies that are critical to detecting illegal activity. Through an in-depth examination of existing methods and case studies, the paper explores how data visualization improves the effectiveness and accuracy of fraud detection systems.The study emphasizes the integration of advanced visualization tools with machine learning algorithms. The goal is to provide insight into how financial institutions can strengthen their defenses against evolving threats. In addition, ethical aspects, usability and the real impact of data visualization in the fight against financial crime are examined. This study contributes to the ongoing discussion about the use of visualization techniques, particularly through Tableau, to strengthen financial systems against illegal activity. Finally, it advocates a proactive and reactive approach to securing financial ecosystems through innovative data visualization strategies.