Enhancing Predictive Analytics in Business Intelligence through Explainable AI: A Case Study in Financial Products

Today, when the importance of data-based decision-making is impossible to question, the use of Explainable Artificial Intelligence (XAI) in business intelligence (BI) has inestimable benefits for the financial industry. This paper discusses how XAI influences predictive analytics in BI systems and how it may improve interpretability, and useful suggestions for financial product companies. Thus, within the context of this study, an XAI framework helps the financial institutions to employ higher-performing and more accurate models, like gradient boosting and neural networks, while sustaining interpretability required in tendentious planning and satisfying governance and supervision necessities. These studies reveal that, as applied to the credit scoring dilemma, XAI techniques such as SHAP and LIME do not only enhance prediction consistency and performance, but also offer a detailed understanding of customer behaviours, risk profiles and product performance. They help in interacting and acting within fields that involve decision making on aspects like customer loyalty, probable risks and audit. Furthermore, the study establishes that by incorporating XAI into BI improves model interpretability, which helps financial experts provide tangible rationale for analytical results and conform to regulatory directives. This framework and findings also support the importance of introducing XAI for financial BI applications to improve analytics practice within the sector. , enabling the generation of higher confidence, reliable decisions, which place the subject of XAI as a profound evolution of business intelligence in finance.