Generative AI platforms are being applied across payments to detect fraud, secure transactions, and personalize experiences while raising new governance and regulatory requirements. Since 2024 the market has shifted from experimental models to integrated, agentic systems that can discover, negotiate and transact on behalf of users — a trend exemplified by payments-focused offerings such as Visa Intelligent Commerce. At the same time, cloud ML platforms and multimodal models (Vertex AI, Google Gemini) provide the model training, deployment and monitoring foundations needed for production-grade scoring, anomaly detection and real‑time decisioning. Enterprise LLM providers and agent platforms (Cohere, IBM watsonx Assistant, Kore.ai, Yellow.ai, Relevance AI) supply private/customizable models, embeddings, retrieval and multi-agent orchestration capabilities that power personalized recommendations, conversational payment flows and automated fraud investigations. Key technical patterns include embedding-based risk scoring, retrieval‑augmented context for transaction histories, multi-agent workflows to combine detectors and human review, and observability/audit trails to support explainability and compliance. Relevance to 2026: regulators and payment networks are tightening requirements around model explainability, data residency, AML/KYC integration and consented data use, making governance, monitoring and secure deployment central to product design. Organizations evaluating these platforms should weigh model privacy, integration with tokenization and strong authentication, real‑time latency, and platform features for logging, policy enforcement and human-in-the-loop controls. In practice, effective deployments pair cloud model infrastructure (Vertex AI/Gemini) or enterprise LLMs (Cohere, watsonx) with agent orchestration (Kore.ai, Relevance AI, Yellow.ai) and payments-native connectors (Visa Intelligent Commerce) to balance personalization, fraud resilience and regulatory compliance.