基于大数据的政策跟踪审计方法研究——以就业政策跟踪审计为例

Research on the Methodology of Big-data-based Real-time Audit of Policy Implementation--Case study on the real-time audit of employment policy implementation

政策跟踪审计是推动国家重大决策部署落实到位,监督检查政策运行实际效果,完善政策内容的重要方式.利用大数据开展政策跟踪审计能够精准评估政策落实效果,更好发挥审计经济体检功能,提升审计监督效能,实现审计监督全覆盖.本文利用Python构建机器学习模型运用于就业政策跟踪审计,通过将技术方法与政策内涵深度结合,开展对数据采集、数据清洗、特征工程、数据建模、可视化分析等核心环节探索,研究关键问题的解决办法,实现了评估政策当前落实情况,预测政策后续执行效果,促进政策优化完善的审计目标,并进一步在新技术应用,审计数据管理,审计成果分享,数据安全等层面提出深化发展建议,推动基于大数据的政策跟踪审计的发展与创新.

Real-time audit of policy implementation is an important way to promote the implementation of major national decisions and arrangements,supervise and inspect the actual effects of policy implementation,and improve policy-making.Using big data technology to carry out real-time audit of policy implementation can accurately evaluate the effect of policy implementation,give better play to the function of government auditing in economic checkups,improve the efficiency of auditing supervision,and achieve audit full coverage.This paper uses Python to build a machine learning model applied to the real-time audit of employment policy implementation,and through the deep combination of technical methods and policy connotation,this paper explores the core links of data collection,data cleansing,feature engineering,data modeling and visual analysis,researches on solutions to key problems,and realizes the audit objectives of evaluating the current implementation of policies,predicting the follow-up implementation effects of the policy,and promoting policy optimization.This paper further puts forward suggestions on deepening development in new technology application,audit data management,audit results sharing,and data security to promote the development and innovation of real-time audit of policy implementation based on big data.