Algorithmic lending has transformed the way finance works today, through an efficiency and risk assessment, enhanced by artificial intelligence through the use of machine learning models in making automated credit scoring decisions.

In this light, the overall aim of this research will be to further investigate probable biases in such models and their consequences on accessibility and fairness. As well as assessing the existing legal framework of the EU and Switzerland concerning algorithmic lending to solve issues related to bias, transparency, human oversight, and providing recommendations to policymakers, financial institutions, and consumers.

Ultimately, this thesis will highlight that algorithmic lending practices are responsibly implemented in the quest for fair and unbiased credit scoring. Highlighting the importance of having a robust legal regulation, human oversight, and transparency in the financial sector to reduce potential biases and discrimination when leveraging algorithmic lending for benefits.