Bias Mitigation Strategies in AI Models for Financial Data

Artificial intelligence (AI) has become integral to financial systems, enabling automation in credit scoring, fraud detection, and investment management. However, the presence of bias in AI models can propagate systemic inequities, leading to ethical, operational, and regulatory challenges. This paper examines strategies to mitigate bias in AI systems applied to financial data. It discusses challenges associated with biased datasets, feature selection, and algorithmic decisions, alongside practical mitigation approaches such as data balancing, algorithmic fairness techniques, and post-processing adjustments. Insights from case studies demonstrate the real-world application of these strategies, highlighting their effectiveness in promoting fairness, enhancing transparency, and reducing adverse outcomes. By providing a comprehensive framework, this paper contributes to fostering equitable financial decision-making.