Artificial Intelligence in Migration Management

6. Conclusion
Annie™ MOORE provides a dynamic allocation approach in refugee resettlement that can vastly increase prospects of finding employment and refugees being placed in communities with the necessary means to help with the integration process. The results of the tests show promising results. As the software learns from previous cases, it can be advantageous in routine cases; this would allow the agency staff to concentrate on cases requiring more attention and a nuanced approach.
Annie™ MOORE has not yet been used on a significant scale date for migration management; as for now, the software does not take into account refugee preferences and mainly focuses on the employment outcome and on whether the location can provide the necessary support for the refugee. Back-testing on the six fiscal years shows promising results, but the real-life results will need much time to be discovered. In addition, acquiring new data will have its own challenges due to the small number of cases each year. This means that the training process will take a long time.
There exists a potential to increase the results by the involvement of agency staff – with manual adjustments to parameters, locations, and settings; the software can be created as a closer representation of real-life situations. The software also drastically reduces the time that is spent in the manual examination, and information is presented more comprehensively; this is a crucial aspect that will allow to improve refugee resettlement majorly. Even if the software has aspects that it could work upon, this is an enormous step in improving migration management.
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