Accuracy and Bias Mitigation in GenAI / LLM-based Financial Underwriting and Clinical Summarization Systems

Praveen Kumar, Shailendra Bade

Abstract: This paper examines the challenges and solutions related to accuracy and bias in Generative AI (GenAI) and Large Language Models (LLMs) when applied to financial underwriting and clinical summarization. We compare and contrast the unique issues in these domains, explore current mitigation strategies, and propose novel approaches to enhance the reliability and fairness of AI-driven decision-making in these critical sectors. Through comprehensive analysis of recent research and case studies, we demonstrate the potential of these technologies to revolutionize both industries while highlighting the crucial need for ongoing vigilance and innovation in addressing accuracy and bias concerns.

Keywords: GenAI, LLM, Generative AI, Large Language Models

How to Cite?: Praveen Kumar, Shailendra Bade, "Accuracy and Bias Mitigation in GenAI / LLM-based Financial Underwriting and Clinical Summarization Systems", Volume 13 Issue 10, October 2024, International Journal of Science and Research (IJSR), Pages: 55-59, https://www.ijsr.net/getabstract.php?paperid=SR24930023705, DOI: https://dx.doi.org/10.21275/SR24930023705