Applications of Artificial Intelligence in Immigration Fraud Detection: A Case Study of Dubai’s GDRFA Systems

Abstract

This study mainly aims to investigate the role and effectiveness of artificial intelligence in terms of detecting the fraud of the residency visa within the “General Directorate of Residency and Foreign Affairs (GDRFA)” in the case of Dubai. Fraud in the case of the residency visa has become one of the concerns in the case of the governments, particularly in the case of different kinds of global cities such as Dubai, where the population become more diverse and demand for the visas increases rapidly. The research approach, such as the “deductive research approach”, will be employed in this study. It will adopt the “primary data collection” method with the help of a structured survey, which is distributed to the different types of key stakeholders within the GDRFA. This research will determine how and to what extent Artificial Intelligence (AI) has been applied and used to detect fraud in residency visas in the General Directorate of Residency and Foreign Affairs (GDRFA).

With the rise in the need to use visas in international cities, doctoring of the forms is a major threat to the immigration systems. It is positivist research with a deductive approach, which embraces the use of structured surveys to gather information on important stakeholders of GDRFA. It looks into the use of AI technologies, including machine learning, biometric verification, and anomaly detection, to detect fraud, and evaluates their effectiveness, ethical implications and limitations in their use. The paper also tests the opinion of the stakeholders and presents recommendations to maximize AI use. Based on the research question posed, the expected results indicate that although the application of AI promotes effective detection and precision of fraud, the quality of data and biases of AI algorithms, confidentiality, and openness remain a matter of concern. The study will add to the knowledge of the incorporation of AI in immigration processes in the population, and the measures of greater ethical and effective use in detecting fraud.