Fair or Flawed? Assessing AI’s Impact on Credit Decisions

The adoption of AI in financial decision-making, especially credit scoring, has sparked concerns about fairness and bias in outcomes. This study examines how biases in AI models affect protected groups, exploring fairness metrics and mitigation techniques to address these challenges. Using industry datasets, it highlights the trade-off between accuracy and equity, showcasing ways to design fairness-aware systems. The findings emphasize transparency, continuous monitoring, and ethical practices as critical for responsible AI use in banking. By addressing bias, financial institutions can ensure inclusive and unbiased credit decision processes, balancing performance with equity in the rapidly evolving landscape of AI-driven finance.