An intelligent social protection service beneficiary selection scheme using machine learning and a mobile application for social safety net program

Social safety net (SSN) initiatives serve an important role in assisting disadvantaged communities and resolving socio-economic inequities across the world. However, Appropriate candidate selection for the SSN program is very difficult due to the lack of a proper automated system. Traditional systems frequently rely on manual evaluation, which can result in mistakes, delays, and unworthy recipients. To conquer this, this paper develops a prediction model to choose beneficiaries and determine eligibility by using five machine learning algorithms. The proposed prediction model can categorise individuals as eligible or ineligible for SSN help using labelled data, such as past program beneficiary records and socioeconomic statistics. The results show that the random forest-based ML algorithm outperforms others in terms of higher accuracy. This paper developed a mobile application through which people can easily apply for SSN programs and see the results. The user rating results provide information on the suitability of the proposed scheme.