• Monu Sharma
• Anushka Raj Yadav
• Shubneet
• Navjot Singh Talwandi

The integration of explainable artificial intelligence (XAI) in high-stakes decision making systems has become critical for fostering trust and regulatory compliance across industries. This study evaluates explainable machine learning models in f inancial applications, focusing on their ability to balance predictive accuracy with interpretability while addressing emerging challenges in fraud detection and digital transactions [1, 2]. Through comparative analysis of techniques like LIME and SHAP, we demonstrate how model-agnostic explanation frameworks enhance transparency in black-box systems without compromising performance. Our findings reveal that strategic implementation of XAI principles improves human-AI collaboration in sensitive domains, particularly when combined with real-time monitoring approaches [3]. The research contributes actionable insights for developing auditable AI systems that meet evolving regulatory requirements and organizational risk management frameworks.

Explainable AI, Model Interpretability, Financial Analytics, Trustworthy ML, Regulatory Compliance

"Enhancing Trust in AI: A Comparative Study of Explainable Machine Learning Models for Critical Decision-Making", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.i391-i395, May-2025, Available :http://www.jetir.org/papers/JETIR2505945.pdf

"Enhancing Trust in AI: A Comparative Study of Explainable Machine Learning Models for Critical Decision-Making", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppi391-i395, May-2025, Available at : http://www.jetir.org/papers/JETIR2505945.pdf