Applications of Supervised Learning Methods in the Fields of Economics and Management
DOI:
https://doi.org/10.54097/ykyett12Keywords:
Supervised learning, Economics and management, Big data analytics, Application effectiveness, Future directions.Abstract
Based on a systematic literature review, this paper explores the current application status, effects, and challenges of supervised learning methods in the fields of economics and management. Grounded in literature from the past five years and utilizing multi-database retrieval and rigorous screening, the study analyzes specific applications of supervised learning across various domains. These include asset pricing and fraud detection in finance, financial fraud identification and audit automation in accounting, and corporate performance prediction and macroeconomic modeling in economics. The research finds that supervised learning, through its advantages of high-precision prediction and automated feature engineering, significantly enhances decision-making efficiency and risk management capabilities in economics and management. However, the field still faces challenges such as data privacy and model interpretability. Future research should focus on directions such as small-sample transfer learning, model transparency, and interdisciplinary integration to further promote development in this area.
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