Embedding a Data-Driven Decision-Making Work Culture in a Social Housing Environment
Karthikeyan, S. Maruyama, T.
Karthikeyan, S.
Maruyama, T.
Publication Date
2024-04
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(c) 2024 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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Abstract
This paper explores the issue of delayed rent payments in social housing in the United Kingdom and its impact on tenants and housing providers. Our approach is to use machine learning algorithms to analyse payment patterns and identify tenants who may be at risk of falling behind on rent payments. By doing this, we aim to equip housing providers with the necessary tools to intervene early and maintain consistent tenancies. We have conducted research using machine learning models such as decision trees and random forests to address this issue. The paper emphasises the potential benefits of Explainable AI, which can help build trust in data-driven decision-making and AI among employees unfamiliar with AI and machine learning.
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Citation
Karthikeyan S, Maruyama T and Sivarajah U (2024) In: Filipe J, Smialek M, Brodsky A et al (Eds) Proceedings of the 26th International Conference on Enterprise Information Systems - Vol 1 (ICEIS 2024) 28-30 Apr 2024. Embedding a Data-Driven Decision-Making Work Culture in a Social Housing Environment. Angers, France. Pp. 807-811.
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Conference paper