Reducing Employment Insurance Backlog: A Machine Learning Approach

Reducing Employment Insurance Backlog: A Machine Learning Approach UPDATE: In July 2023, the implementation of the Machine Learning model proved successful, resulting in the completion of over 40,000 claims. Although this page won't receive further updates, the project is still ongoing and can be accessed [here](https://open.canada.ca/data/en/info/6b429c8e-ee5b-451a-883f-b6180ada9286). It's now referred to as the “Employment Insurance Machine Learning Workload”. ----- The COVID 19 pandemic has triggered an unprecedented volume of Employment Insurance (EI) claims and associated Claim Review Work. During the pandemic, the focus on implementing the Emergency Response Benefit (ERB), Simplified EI, and the subsequent return to regular EI has resulted in a backlog of claim reviews, many of them for claims established before March 2020, which are competing for resources with more current and pressing work. The implementation of the Pre-ERB EI Recalculation Outcome Prediction Machine Learning (ML) model seeks to minimize the number of older claims (pre-March 2020) requiring review by an officer by using the model to predict the most probable outcome of each recalculation and triaging the associated work items accordingly, with recalculations which are unlikely to impact the claimants. The model has been developed by Employment Insurance Program Performance in consultation with several stakeholders within the EI program. A Random Forest model was applied to data from EI Production systems. This project has been conducted with oversight from the Artificial Intelligence Centre of Excellence in accordance with the Treasury Board of Canada Secretariat guidelines. It has undergone a peer review process. Legal services were engaged to review the project under the terms of the Directive on Automated Decision-Making Systems which took effect on April 1, 2020. 2024-04-27 Employment and Social Development Canada morgan.goddard@servicecanada.gc.ca Government and PoliticsAIAAIAArtificial IntelligenceEIMachine LearningRandom ForestERB Algorithmic Impact Assessment - Employment Insurance (E.I) Backlog ReductionPDF https://open.canada.ca/data/dataset/24d2cab2-6a0d-4234-9239-b6ce102ebabd/resource/437a8cc2-da3a-4ed7-abb1-d45dfd7af0e3/download/aia_ei_backlog_reduction.pdf Évaluation d'Incidence Algorithmique - Réduction de l'arriéré de l'Assurance-Emploi (A.E)PDF https://open.canada.ca/data/dataset/24d2cab2-6a0d-4234-9239-b6ce102ebabd/resource/7a199b11-9b8c-480c-88bd-ec9125b3bfaf/download/aia_ae_reduction_inventaire.pdf Json filesJSON https://open.canada.ca/data/dataset/24d2cab2-6a0d-4234-9239-b6ce102ebabd/resource/8c0e7774-3532-48a7-8da9-e93076e3c26d/download/aia-results_second_assesment.json

UPDATE:

In July 2023, the implementation of the Machine Learning model proved successful, resulting in the completion of over 40,000 claims. Although this page won't receive further updates, the project is still ongoing and can be accessed here. It's now referred to as the “Employment Insurance Machine Learning Workload”.

The COVID 19 pandemic has triggered an unprecedented volume of Employment Insurance (EI) claims and associated Claim Review Work. During the pandemic, the focus on implementing the Emergency Response Benefit (ERB), Simplified EI, and the subsequent return to regular EI has resulted in a backlog of claim reviews, many of them for claims established before March 2020, which are competing for resources with more current and pressing work.

The implementation of the Pre-ERB EI Recalculation Outcome Prediction Machine Learning (ML) model seeks to minimize the number of older claims (pre-March 2020) requiring review by an officer by using the model to predict the most probable outcome of each recalculation and triaging the associated work items accordingly, with recalculations which are unlikely to impact the claimants.

The model has been developed by Employment Insurance Program Performance in consultation with several stakeholders within the EI program. A Random Forest model was applied to data from EI Production systems.

This project has been conducted with oversight from the Artificial Intelligence Centre of Excellence in accordance with the Treasury Board of Canada Secretariat guidelines.

It has undergone a peer review process. Legal services were engaged to review the project under the terms of the Directive on Automated Decision-Making Systems which took effect on April 1, 2020.

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