Article
UNCOVERING MALICIOUS BOTS THROUGH BEHAVIOR-BASED TRANSITION PROBABILITY FEATURES
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 decision-making 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 high-stakes 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 decision-making, 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 decision-making, analyzing existing systems, identifying their limitations, and proposing an XAI-driven framework that balances accuracy with interpretability. The research highlights how explainability can transform AI from a purely predictive tool into a responsible decision-support system aligned with ethical, legal, and operational requirements of the financial sector.
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