Application of big-data lending in the banking industry and strategic recommendations

Building on an analysis of the current state of big-data lending applications, this paper systematically reviews related research progress, probes in depth the core differences between the operating models of traditional banks and internet banks (with particular attention to Xinwang Bank, XWBank) and the challenges they face, and accordingly proposes optimization strategies. Leveraging massive datasets and advanced technologies, big-data lending markedly enhances the inclusiveness of credit services and the effectiveness of risk management. The study finds that, although traditional banks are actively deploying in this field, they remain constrained by issues such as single-source data and rigid processes; internet banks, by contrast, demonstrate advantages through ecosystem data and agile operating models. XWBanks practice of full customer-segment coverage, end-to-end online processes, and real-time risk management offers an important reference for the industry. This paper recommends that commercial banks expand the breadth of data dimensions, build differentiated rating systems, optimize approval processes, and deepen cooperation with internet banks to achieve complementary strengths, thereby effectively addressing challenges and advancing inclusive finance.