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Using Machine Learning to Create an Early Warning System for Welfare Recipients

Using high-quality nation-wide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018.

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Using Machine Learning to Create an Early Warning System for Welfare Recipients by Dr Dario Sansone, Lecturer in Economics, University of Exeter Business School

We show that machine learning algorithms can significantly improve predictive accuracy compared to simpler heuristic models or early warning systems currently in use. Specifically, the former predicts the proportion of time individuals are on income support in the subsequent four years with greater accuracy, by a magnitude of at least 22% (14 percentage points increase in the R2), compared to the latter. This gain can be achieved at no extra cost to practitioners since the algorithms use administrative data currently available to caseworkers. Consequently, our machine learning algorithms can improve the detection of long-term income support recipients, which can potentially provide governments with large savings in accrued welfare costs and allow institutions to offer timely support to these at-risk individuals. To register, please click here. Registration will close: 22 September at 09:00.

Whilst we appreciate the flexibility that hybrid delivery brings, we would encourage you to come in person where there will be tea and coffee afterwards.

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