In a healthcare landscape marked by rising costs, administrative complexity, and increasing demands on both professionals and patients, financial competence has become a critical yet often overlooked component of effective care. Educational systems powered by explainable artificial intelligence (XAI) and informed by large-scale data analytics offer new opportunities for developing this capacity in ways that are both personalized and transparent.

The paper presents several key strategies. First, XAI allows educational platforms to go beyond delivering automated recommendations by offering clear, human-understandable explanations of how those decisions are made. This fosters trust, critical thinking, and learner autonomy, particularly important when financial decisions intersect with patient care. Second, big data technologies enable these systems to draw from a broad range of financial and healthcare sources, including treatment costs, insurance claims, and institutional spending patterns. Such data allows for adaptive learning paths tailored to learners’ professional roles, levels of experience, and real-world financial contexts. Third, interactive simulations and scenario-based learning modules can expose users to realistic decision-making situations, reinforcing both conceptual understanding and practical competence.

These educational approaches have practical applications for a range of users: healthcare administrators managing institutional budgets, clinicians making cost-informed care decisions, and patients seeking to understand their medical bills and insurance options. By offering transparent, data-informed, and context-sensitive learning experiences, XAI and big data can make financial education more effective, accessible, and meaningful.

At the same time, the paper acknowledges significant challenges. These include concerns around data privacy, potential biases in algorithmic learning systems, unequal access to digital infrastructure, and the absence of clear regulatory standards for educational uses of artificial intelligence. Without careful design and oversight, there is a risk of deepening existing inequalities or undermining trust in educational and healthcare institutions.

The paper concludes by emphasizing the need for a thoughtful and ethical integration of explainable artificial intelligence and big data into financial education for healthcare. This includes investment in secure digital platforms, interdisciplinary educator training, and policy frameworks that promote transparency, accountability, and fairness. If implemented with care, such tools can contribute meaningfully to a more financially literate and equitable healthcare system, empowering individuals to make informed choices and strengthening institutions through better-informed financial decision-making.

Keywords: Financial Education, Healthcare, Explainable Artificial Intelligence, Big Data.