Spot the bottleneck: Using technology to identify inefficiencies in RSD procedures
Challenge
Processing times for Refugee Status Determination (RSD) cases vary greatly, often for known reasons, such as different operational contexts, processing strategies, and caseloads. However, even when all known variables are controlled for, there remain discrepancies in processing time, as well as bottlenecks, that are not easily explained.
Solution
Leverage artificial intelligence to analyze the process flow, taking into account multiple variables, to identify patterns impacting the case processing time. Understanding systematic bottlenecks could help inform effective solutions.
Impact
Major bottlenecks and their root causes in the RSD process identified, helping to shape potential solutions. Reducing delays and inefficiencies allows for better UNHCR resource management and ensures refugee applicants secure the rights associated with this status more quickly.
Project impact
Other information
UNHCR conducts Mandate Refugee Status Determination (RSD) in around 40+ countries every year, but processing times vary widely across operations and even within the same caseload, leaving asylum seekers waiting months, sometimes years, for a decision without a clear understanding of where the delays originate. Funded by the Data Innovation Fund and led by UNHCR's Asylum and Status Determination Section together with Global Data Service, this project develops the first AI-powered RSD Bottlenecks Platform, combining process mining, machine learning and predictive analytics to identify delays at every stage from Registration to Decision. Piloted in Malaysia, Morocco and Algeria, the platform enables RSD managers to simulate the impact of staffing changes, population influxes and modality shifts, flag long-pending cases and receive recommendations to minimise waiting periods. The model is replicable across all UNHCR RSD operations and contributes directly to faster, fairer and more accountable asylum procedures globally. Read more about this project in an Anadolu Agency report.