This topic covers the AI tools and platforms used to automate banking and finance operations — from customer-facing virtual agents and contact-center automation to payments integrations, multi-agent orchestration, model hosting, and regulatory governance. Adoption accelerated after 2024–2025 advances (including payments capabilities for AI agents), pushing banks to combine secure model infrastructure with observability and vendor governance. Key categories include enterprise virtual assistants and orchestrators (e.g., IBM watsonx Assistant and orchestration layers), managed ML/GenAI platforms for training and deployment (Google’s Vertex AI), enterprise LLM providers for private, customizable models (Cohere), and specialized orchestration/agent platforms (Kore.ai). Payment and commerce integrations such as Visa Intelligent Commerce (introduced in 2025) enable programmatic purchasing and tighter payments flows for agent-driven experiences. Contact-center and conversational intelligence platforms (Observe.AI) add voice agents, real-time agent assist, and automated quality assurance. Governance and compliance tooling (Monitaur and similar offerings) centralize policy, monitoring, validation, and vendor oversight required by regulated finance and insurance environments. Why it matters in 2026: banks are scaling use of LLM-based assistants and multi-agent workflows for customer service, fraud triage, back-office automation, and embedded payments, while regulators and auditors require explainability, access controls, and vendor governance. Practical deployments prioritize private/custom models, end-to-end observability, secure payment integrations, and no-code to pro-code orchestration that can be audited. Evaluations should therefore consider model privacy, integration with banking/payment rails, orchestration capabilities, contact-center support, and governance/monitoring features rather than just raw model performance.