International Journal of Science and Research (IJSR) ISSN: 2319-7064 SJIF (2020): 7.803 Volume 10 Issue 9, September 2021 www.ijsr.net Licensed Under Creative Commons Attribution CC BY Adaptive XAI Narratives for Dynamic Fraud Detection: Keeping AI Explanations Clear as Models Evolve Rajani Kumari Vaddepalli Milwaukee, Wisconsin, USA rajani[dot]vaddepalli15[at]gmail.com Abstract: AI models used for fraud detection are constantly updated to tackle new threats, but their explanation methods often stay static, leading to outdated or misleading interpretations. This research explores how adaptive explainable AI (XAI) can generate real- time, accurate explanations that evolve alongside the models they describe. We introduce a framework for self-updating narrative generation, combining retrieval-augmented generation (RAG) and meta-learning to ensure explanations stay aligned with the latest model behavior and emerging fraud patterns. Testing on real-world transaction data, we compare adaptive narratives against traditional static explanations, measuring robustness, response time, and user understanding. Our results show that adaptive XAI not only preserves transparency in fast-changing fraud environments but also builds stronger trust among users, auditors, and regulators. This work offers a practical solution for real-time interpretability in AI-driven fraud detection-a critical need for deployable, trustworthy systems. Keywords: Explainable AI (XAI), Fraud Detection, Dynamic Model Interpretability, Adaptive Explanations, Real-Time Decision Making, Retrieval-Augmented Generation (RAG), AI Transparency, Financial Cybersecurity, Robust Machine Learning, Regulatory Compliance 1.Introduction Artificial intelligence (AI) has become a frontline defense against financial fraud, but its effectiveness hinges on one critical factor: trust. While models evolve rapidly to detect new fraud patterns, their explanations often freeze in time- like a snapshot that grows increasingly inaccurate. This disconnect undermines confidence among auditors, regulators, and even the AI teams tasked with maintaining these systems. Consider a fraud detection model trained in 2020 to flag credit card scams. By 2024, it might adapt to recognize synthetic identity fraud or deepfake-driven attacks, but if its explanations still reference outdated features (e.g., "high- risk transaction due to geographic distance"), users are left confused or misled. This problem isn’t hypothetical. [1] studied 12 major banks in 2018 and found that 67% of fraud analysts distrusted AI tools when explanations didn’t match current fraud patterns. Meanwhile, [2] showed that static XAI methods (e.g., SHAP, LIME) could degrade in accuracy by up to 40% within six months of model updates in dynamic environments. The Lag Between Models and Explanations Fraud detection operates in a high-stakes, fast-moving landscape. Traditional XAI tools generate explanations once-typically when the model is deployed-but fraudsters innovate daily. For example, [1] documented how criminals exploited COVID-19 relief programs by rapidly shifting tactics, rendering pre-pandemic fraud models (and their explanations) obsolete. Static XAI fails here because it can’t "learn" alongside the model. Why Adaptability Matters The demand for real-time explanations isn’t just technical; it’s legal and ethical. Regulations like GDPR grant users the "right to explanation" for automated decisions, but compliance is impossible if those explanations are based on a model’s past behavior. [2] demonstrated this in loan approval systems, where outdated SHAP analyses wrongly attributed rejections to income level, while the actual model had shifted to prioritize transaction velocity. Such gaps create liability risks and erode public trust. Toward Self-Updating XAI This paper argues for adaptive XAI narratives-explanations that evolve as models do. Drawing from [1]’s insights on analyst needs and [2]’s work on explanation drift, we propose a framework that couples real-time narrative generation with model updates. Unlike prior work, our approach prioritizes: Timeliness: Aligning explanations with the latest model behavior (e.g., via retrieval-augmented generation). Interpretability: Balancing technical rigor with clarity for non-experts (auditors, customers). Auditability: Creating a "paper trail" of explanation versions for regulatory compliance. Contributions Our research builds on [1]’s findings about user trust and [2]’s technical groundwork to offer: A method for continuous explanation updates without manual intervention. Evidence that adaptive XAI reduces misunderstanding among end-users (e.g., fraud investigators). A scalable solution for financial institutions facing regulatory scrutiny. Paper ID: SR21923114959 DOI: https://dx.doi.org/10.21275/SR21923114959 1819