BAMMIDI SONIKA, GOKURULA DEEPIKA, BADRI SASIKIRAN, BOMMALI RAJA SEKHAR, B. KUSUMAKUMARI 03 03 Explainable Artificial Intelligence XAI has emerged as a critical paradigm in modern financial systems where transparency accountability and trust are essential requirements Traditional artificial intelligence and machine learning models particularly deep learning techniques have demonstrated exceptional predictive performance in financial decisionmaking tasks such as credit scoring fraud detection risk assessment and algorithmic trading However these models often function as black boxes providing limited insight into how decisions are derived This lack of interpretability raises serious concerns among regulators financial institutions and end users especially in highstakes environments where decisions can significantly impact individuals and organizations XAI addresses this challenge by incorporating techniques that make AIdriven decisions understandable interpretable and justifiable to human stakeholders without compromising performance In financial decisionmaking XAI enables institutions to explain why a loan application was approved or rejected how risk scores are calculated or why a transaction is flagged as fraudulent Such transparency is essential for regulatory compliance with laws such as GDPR fair lending practices and ethical AI standards Moreover XAI enhances stakeholder trust reduces bias and supports better governance by allowing financial analysts and auditors to validate and monitor AI behavior This study focuses on the role of XAI in enabling transparent financial decisionmaking analyzing existing systems identifying their limitations and proposing an XAIdriven framework that balances accuracy with interpretability The research highlights how explainability can transform AI from a purely predictive tool into a responsible decisionsupport system aligned with ethical legal and operational requirements of the financial sector Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026