Asset Impairment Prediction in the Age of Big Data and Machine Learning

Abstract

 In the context of a globalized economy, accurately predicting asset impairment is critical for maintaining the financial health of enterprises and guiding investment decisions. This paper provides a comprehensive review of the internal and external factors influencing asset impairment and explores how these factors collectively impact asset valuation. It examines asset impairment prediction methods from two perspectives: traditional financial analysis and emerging machine learning techniques. While conventional approaches rely on cash flow forecasting and discounting, machine learning leverages historical data to identify patterns and trends in asset impairment. Machine learning provides distinct advantages compared to traditional methods, such as its capacity to handle large datasets, capture non-linear relationships, and adapt to market fluctuations. These capabilities notably enhance the accuracy and reliability of asset impairment predictions, enabling enterprises to make better-informed investment and operational decisions in increasingly complex market circumstances. Furthermore, these advancements provide valuable insights for accounting standard setters and policymakers. With ongoing advancements in big data and computing power, machine learning holds immense potential in asset impairment forecasting. Its application is expected to enhance risk management for enterprises, paving the way for more effective decision-making and sustainable growth.