An Innovative Paradigm of Cross-Border Financial Education Based on Reverse Engineer Perspective - A Case Study of Big Data Credit Scoring

Abstract

Addressing the cognitive challenge of the algorithm black box faced by business students in the context of the New Liberal Arts, this study proposes a novel experiential learning paradigm based on reverse engineering. Leveraging the Zhima Credit scoring modeling project, the study employs parametric scaffolding instruction to guide 27 business students with no programming background through the reverse deconstruction and value reconstruction of credit models. The results indicate that this model significantly enhanced students’ sensitivity to unstructured data and comprehension of risk control logic, while effectively awakening their ethical consciousness regarding the mitigation of algorithmic discrimination. This study confirms that Reverse Engineer Learning effectively breaks disciplinary barriers, providing a pedagogical paradigm for cultivating composite financial talents who possess both technical rationality and humanistic care.