Learning from machines to close the gap between funding and expenditure in the Australian National Disability Insurance Scheme

Abstract

The Australian National Disability Insurance Scheme (NDIS) allocates funds to participants for purchase of services. Only one percent of the 89,299 participants spent all of their allocated funds with 85 participants having failed to spend any, meaning that most of the participants were left with unspent funds. The gap between the allocated budget and realised expenditure reflects misallocation of funds. Thus we employ alternative machine learning techniques to estimate budget and close the gap while maintaining the aggregate level of spending. Three experiments are conducted to test the machine learning models in estimating the budget, expenditure and the resulting gap; compare the learning rate between machines and humans; and identify the significant explanatory variables. Results show that machines learn “faster” than humans; machine learning models can improve the efficiency of the NDIS implementation; and significant explanatory variables identified by decision tree models and regression analysis are similar.

Keywords

• National Disability Insurance Scheme
• Disability insurance
• Budget
• Decision making
• Data analytics
• Machine learning