• AI Act
• algorithmic lending
• creditworthiness
• GDPR
• law

Algorithmic lending that utilized automated credit scoring decisions through the use of machine learning models has changed the financial landscape by an alleged efficiency and enhanced risk assessment. Although issues with fairness and possible biases in these models have surfaced. The function of algorithmic lending in the banking industry will be examined in this master’s thesis.
In order to address concerns about fairness and ensure the responsible deployment of credit scoring and algorithmic lending, explores potential biases into potential biases inherent in these models and how they affect loan access and fairness, evaluates the effectiveness of current legal frameworks in the EU and Switzerland regarding algorithmic lending in mitigating bias, analyses potential solutions to promote transparency, human oversight, and reduce bias in algorithmic lending practices, and develops recommendations for policymakers, financial institutions, and consumers.
This thesis aims to highlight the need for responsible implementation of algorithmic lending practices that guarantee fair and unbiased credit scoring. Through the promotion of robust legal frameworks, human oversight and transparency, the financial sector can leverage the minimization of potential for bias and discrimination while benefiting from algorithmic lending.