As machine learning systems increasingly shape financial decision-making, concerns have emerged regarding algorithmic bias in credit-scoring models. This study investigates whether predictive models for credit default risk disproportionately affect demographic and socioeconomic groups, potentially leading to unfair credit denials. To address this, a fairness-aware machine learning framework will be developed, considering structural relationships among variables.

 The study analyzes different algorithms and datasets, including the credit models used by the Board of Governors of the Federal Reserve System, the Default of Credit Card Clients dataset from the UCI Machine Learning Repository, and Kaggle datasets such as Home Credit Default Risk and Give Me Some Credit, utilized by Home Credit to promote financial inclusion, among others. It compares performance across these datasets and conducts thematic analyses to identify variables linked to unfair algorithms. By examining model coefficients and feature importance measures, this research assesses the extent to which algorithmic credit assessment may reinforce existing inequalities. The study explores how the linear independence of variables, such as demographics, can significantly influence risk percentage calculations and examines how certain variables, such as peer-based features that tie default risks to the profiles of neighboring individuals, can promote financial exclusion. 

After completing the analysis, a machine learning model will be developed to illustrate how a fairer model would perform, comparing its results with those of existing models to evaluate efficiency. Finally, a market analysis will be conducted to assess the long-term effects of implementing the new model, aiming to demonstrate how a fairer model would benefit society through market inclusion, thereby fostering the economy.