Construction and Application of Financial Early Warning Model for High-Tech Enterprises in the Era of Big Data

Abstract

With the in-depth development of the global digital economy, high-tech enterprises, as the core engine of the national innovation-driven strategy, are facing unprecedented market competition and financial risk challenges. Traditional financial early warning research is mostly limited to static analysis of financial statement data, and there are shortcomings such as lag in timeliness, single data dimension, and inability to identify nonlinear risk signals. In the context of the era of big data, multi-source heterogeneous data provides new possibilities for enterprise risk perception. This paper systematically sorts out the financial operation characteristics of high-tech enterprises, and proposes a multi-dimensional early warning index system that integrates financial indicators, non-financial indicators and external macro big data. In terms of model construction, the path of processing high-dimensional data using ensemble learning algorithms (such as XGBoost and random forest) is deeply discussed, and the early warning logic of dynamic iteration is constructed. Through empirical testing, it is found that the big data early warning model is better than the traditional statistical model in terms of sensitivity and accuracy of crisis identification. Finally, this paper puts forward safeguard measures for how high-tech enterprises implement the big data financial monitoring system, aiming to provide accurate risk decision-making support for enterprises to achieve high-quality development.