Algorithmic Credit Scoring in Vietnam: A Legal Proposal for Maximizing Benefits and Minimizing Risks

Abstract

Artificial intelligence (AI) and big data are transforming the credit market around the world. Algorithmic credit scoring (ACS) is increasingly used to assess borrowers’ creditworthiness, using technology to glean non-traditional data from smartphones and analyze them through machine-learning algorithms. These processes promise efficiency, accuracy, and cost-effectiveness compared with traditional credit scoring. However, this technology raises public concerns about opacity, unfair discrimination, and threats to individual privacy and autonomy. Many countries in Southeast Asia are introducing ACS in consumer finance markets, although—even with the significant concerns raised—there is an ongoing and concerning lag in oversight and regulation of the process. Regulation is vital to delivering big data and AI promises in the financial services market, while ensuring fairness and public interest. This article utilizes Vietnam, where the lending industry deploys ACS but in a situation of legal limbo, as a case-study to analyze the consequences of this technology. Vietnam is one of the foremost Southeast Asian countries in which ACS usage is spreading rapidly, and this provides an excellent opportunity to review the regulation, or lack thereof, and determine the implications that this may have for other countries that are currently introducing ACS in consumer finance markets. The article concludes with a proposal to regulate ACS in Vietnam based on international regulation and guidelines on ACS, data privacy, and AI to enable a transparent, accessible, and fair process.