A System Model for Valuing Data Assets in Commercial Banks

With the ongoing development of the digital economy, the productive function of data as an economic factor has become increasingly salient. Scientifically and rigorously assessing the value of data assets is essential for improving the national economic accounting system and promoting sustainable economic growth. In light of the limitations inherent in existing cost-based and market-based valuation approaches, this paper proposes a comprehensive valuation model that integrates the cost approach with the income approach and applies it to the commercial banking sector. Specifically, text analysis is employed to estimate human capital investment in data assets from the perspective of labor supply and demand, after which total costs are derived based on the proportion of human capital. An ARIMA model is used to forecast future cost inputs and net profits associated with data assets. Furthermore, the income-based approach is adopted to estimate the average present value of data assets, with the results of the two methods serving to validate each other. The comparison of estimation results under the cost approach and the income approach further validates the relationship between input and output in data assets. This also demonstrates that data assets follow the law of diminishing marginal utility, thereby contradicting the notion that data increases in value with greater usage. This study enriches the theoretical framework of data asset valuation, broadens its application scope, and provides meaningful guidance for advancing data asset accounting practices and related research.