# FairRAG: A Privacy-Preserving Framework for Fair Financial Decision-Making
## 1 Introduction  
The use of machine learning as a means to address critical issues in the financial domain has transformed our understanding of the underlying patterns in data. However, in this field, which is highly dependent on user data and personally identifiable information in the decision-making process, it is difficult to ensure that users' information is protected and that the models are making unbiased decisions. Human-centered problems in finance, such as credit approvals and insurance underwriting, have seen an increase in the use of ML algorithms [1, 2]. Although efficient, these use cases are highly susceptible to discriminatory decisions [3]. The existing research attempts to balance model performance with fairness and privacy guarantees; it faces challenges to accurately address every potential issue [4, 5]. With official regulations such as the European General Data Protection Regulation [6] andthe California Consumer Privacy Act [7] becoming increasingly prevalent, organizations must adhere to strict requirements. This ultimately requires significant adjustments to the model training workflows. In general, various studies have highlighted the importance of making informed decisions when working with customer financial data, particularly concerning influencing loan decisions and unfair credit scoring models [1; 8]. Instances where bias and data protection were not considered in financial-related ML tasks have led to inequality. Black applicants had their mortgage applications denied at twice the rate of white applicants [9]. Similarly, customer churn models have been found to make different predictions between men and women [10]. Regarding training data privacy, external adversaries use various attacks [11] to obtain information about individuals present in a training dataset [12]. These concerns underscore the need to develop comprehensive frameworks that deliver accurate predictive performance while also addressing fairness, privacy, and regulatory compliance in their output. To confront these concerns, research on privacy-preserving mechanisms, such as differential privacy [1; 13], is applied to ensure that individual data contributions remain untraceable and that fairness is promoted throughout both the ML training and post-training processes. As various studies have demonstrated, we can integrate DP with existing models to address privacy and bias [14; 15]. Both large language models and retrieval-augmented generation architectures have been individually explored in a DP context [16]. Both frameworks excel at understanding complex datasets; however, LLMs display significant challenges that raise concerns about their suitability for handling sensitive data, such as biases in pre-trained models, hallucinations, and a lack of explainability [17; 18]. It is possible to combine RAG with LLM pattern recognition capabilities to address these shortcomings by reducing hallucinations and bias while maintaining high accuracy [19]. We explore the possibility of a customer churn prediction framework that ensures accuracy, addresses fairness metrics, and includes privacy-preserving features on the Bank Churn [20] and Telco Churn [21] customer datasets. Our method introduces a specific FairRAG model: an LLM with an RAG embedding layer trained with DP to generate a fair and privacy-protected synthetic dataset that can be used in a secure ML training pipeline. We build on previous research that leverages randomness in the next token generations of LLMs for DP guarantees [15]. The existing literature on LLMs focuses mainly on generating synthetic text or image data rather than tabular datasets [22; 23; 24]. Our framework builds on these methods by utilizing RAG to maintain a knowledge base of the data, enabling more structured application while ensuring the privacy and fairness of training data in downstream tasks.