Ethical Frameworks for AI Credit Scoring

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As the financial industry continues to evolve, algorithms are playing an increasingly important role in determining who receives loans, the interest rates they pay, and the conditions attached to their borrowing. From mortgage applications to credit card approvals, these systems assess borrowers’ creditworthiness by analyzing large amounts of data, offering potential benefits like greater efficiency, improved accuracy, and broader financial inclusion. However, this shift from human to algorithmic decision-making raises important ethical concerns around fairness, transparency, and accountability in the financial sector.

The growing prominence of AI in credit decision-making represents a fundamental paradigm shift. Traditional credit scoring systems primarily analyzed conventional financial history, creating a standardized but limited view of creditworthiness. Modern AI-powered algorithms incorporate alternative data sources—from utility payments to digital footprints—expanding their reach beyond conventional metrics (Faggella, 2020). This technological evolution promises to reach previously «credit invisible» populations but introduces complex ethical considerations.

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