Research on Algorithmic Bias Risks and Their Regulation in the Digital and Intelligent Transformation of Finance
Abstract
With rapid digital economy growth, AI, big data and machine learning are widely applied in finance, pushing financial institutions
toward digital intelligent transformation. Algorithmic technologies improve risk management, precision marketing, credit assessment and
investment decisions, yet algorithmic bias stands out. Caused by data imbalance, technical defects or profit motives, it triggers discriminatory results, harming consumer rights, market fairness and financial security. This paper explores its risks, causes and manifestations, and puts
forward regulatory solutions. It finds algorithmic bias involves technical, legal, ethical and governance challenges. Boosting algorithmic transparency, data governance, regulatory systems and industrial co governance can mitigate such risks and advance sound fintech development.
toward digital intelligent transformation. Algorithmic technologies improve risk management, precision marketing, credit assessment and
investment decisions, yet algorithmic bias stands out. Caused by data imbalance, technical defects or profit motives, it triggers discriminatory results, harming consumer rights, market fairness and financial security. This paper explores its risks, causes and manifestations, and puts
forward regulatory solutions. It finds algorithmic bias involves technical, legal, ethical and governance challenges. Boosting algorithmic transparency, data governance, regulatory systems and industrial co governance can mitigate such risks and advance sound fintech development.
Keywords
Financial Digital Intelligence; Algorithmic Bias; Fintech; Data Governance; Algorithmic Regulation
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DOI: http://dx.doi.org/10.70711/memf.v3i8.9708
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