The rapid integration of artificial intelligence into financial services has transformed decision-making processes, operational efficiency, and customer relationship management. However, this technological transformation has simultaneously introduced novel risks, including algorithmic bias, deepfake-enabled fraud, and large-scale digital misinformation campaigns that can destabilize financial ecosystems. Recent incidents involving AI-generated impersonation in financial transactions demonstrate the urgency of developing robust analytical and governance frameworks capable of identifying, preventing, and mitigating such threats. This research develops a comprehensive theoretical framework that integrates causal inference methodologies, uplift modeling, and responsible AI governance to strengthen decision-making systems within financial institutions. Drawing upon interdisciplinary literature from marketing analytics, statistical causal inference, and AI governance policy, the study explores how advanced analytics can be employed not only to predict customer behavior but also to detect manipulation, fraud, and misinformation that increasingly target financial infrastructures.

The study synthesizes foundational work in causal modeling, including propensity score matching and matched sampling approaches, with contemporary developments in marketing analytics and financial AI systems. These analytical techniques are contextualized within emerging regulatory frameworks such as the European Union Artificial Intelligence Act and responsible AI initiatives promoted by global regulatory bodies. The research also examines real-world cases of deepfake-enabled financial fraud and analyzes the implications for risk governance and institutional trust. Through an extensive conceptual methodology, the article proposes a multi-layered decision architecture that combines predictive analytics, causal inference, and governance oversight mechanisms. This architecture enables financial institutions to evaluate not only the probability of events but also the causal impact of interventions, thereby improving the precision of customer targeting, fraud detection, and policy compliance.

The findings suggest that integrating causal inference methods with modern AI governance strategies significantly improves the robustness of financial decision engines. Institutions adopting these approaches can more effectively distinguish correlation from causation in customer analytics, thereby enhancing strategic marketing decisions while simultaneously reducing systemic vulnerabilities to AI-driven deception and disinformation. Furthermore, the study highlights the importance of regulatory collaboration and ethical oversight to ensure that AI systems operate transparently and responsibly. The research contributes to the growing body of knowledge on AI governance in finance by bridging methodological advances in analytics with emerging policy frameworks designed to safeguard digital economies.

Artificial intelligence governance, causal inference, financial analytics, deepfake fraud

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