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首页> 外文期刊>International journal of forecasting >Preventing rather than punishing: An early warning model of malfeasance in public procurement
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Preventing rather than punishing: An early warning model of malfeasance in public procurement

机译：预防而不是惩罚：公共采购中的渎职预警模型

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摘要

Is it possible to predict malfeasance in public procurement? With the proliferation of e-procurement systems in the public sector, anti-corruption agencies and watchdog organizations have access to valuable sources of information with which to identify transactions that are likely to become troublesome and why. In this article, we discuss the promises and challenges of using machine learning models to predict inefficiency and corruption in public procurement. We illustrate this approach with a dataset with more than two million public procurement contracts in Colombia. We trained machine learning models to predict which of them will result in corruption investigations, a breach of contract, or implementation inefficiencies. We then discuss how our models can help practitioners better understand the drivers of corruption and inefficiency in public procurement. Our approach will be useful to governments interested in exploiting large administrative datasets to improve the provision of public goods, and it highlights some of the tradeoffs and challenges that they might face throughout this process. (C) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译：是否有可能在公共采购中预测渎职行为？随着公共部门的电子采购系统的扩散，反腐败机构和看门狗组织可以获得有价值的信息来源，以确定可能成为麻烦的交易以及为什么。在本文中，我们讨论了利用机器学习模式预测公共采购中效率低下和腐败的承诺和挑战。我们用哥伦比亚的数据集说明了这种方法，在哥伦比亚有超过200万公共采购合同。我们培训了机器学习模型，以预测哪些将导致腐败调查，违反合同或实施效率低下。然后，我们讨论我们的模型如何帮助从业者更好地了解公共采购腐败的驱动因素和效率低下。我们的方法将对有兴趣利用大型行政数据集的政府，以改善提供公共物品，它突出了他们在整个过程中可能面临的一些权衡和挑战。 （c）2020国际预测研究所。由elsevier b.v出版。保留所有权利。

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