Research on Credit Empowerment Based on Big Data and Digital Footprints

Abstract

Under the background of the deep development of the digital economy, the traditional credit evaluation relies too much on asset mortgage and financial reports, which leads to information lag and makes it difficult to cover the "white credit" group. This limitation promotes the transformation of credit evaluation to "predictive insight" driven by big data and digital footprint. This paper systematically discusses this enabling mechanism, covering the technological evolution path from data fusion, feature engineering, to deep learning, graph neural network, and federated learning, and analyzes its wide application in financial credit, social mortgage-free leasing, and metacosmic decentralized identity. The study found that big data empowerment significantly improved the fidelity of risk identification. By giving financial value to the digital trajectory, it eliminated the mortgage bias of traditional credit and created opportunities for inclusive participation for vulnerable groups. At the same time, credit scoring is evolving from a single economic constraint to an "algorithmic contract" that promotes social transformation and digital survival. Despite the challenges of algorithmic discrimination, model black box and privacy paradox, this paper points out that the future credit system should be transformed to a "trusted AI" framework, combining regulatory Technology and privacy computing technology to achieve deep decoding and governance optimization of credit value on the premise of ensuring data sovereignty.