AI‑Driven Fraud & Chargeback Solutions cover systems that detect, prevent and automate responses to fraudulent payments and customer chargebacks. Providers such as Chargeflow (chargeback automation and dispute workflow), SEON (transaction, device and identity intelligence), and xpate (payment risk analytics) are increasingly complemented by platforms that supply models, orchestration, analytics and conversational automation. This category sits at the intersection of AI security governance, regulatory compliance, and data analytics. Relevance in 2025 stems from higher transaction volumes, faster real‑time payments, tighter consumer protection and cross‑border regulatory scrutiny — all of which raise the cost of disputes and the need for explainable, auditable decisioning. Modern solutions combine real‑time risk scoring with retrieval-augmented evidence, automated dispute submission, and human-in-the-loop review to reduce false positives while preserving compliance and audit trails. Key supporting tools and roles: Kore.ai and IBM watsonx Assistant provide governed, enterprise virtual agents and multi‑agent orchestration for automating dispute handling and customer interactions; Alteryx and Sisense supply no‑code/low‑code analytics, feature engineering and embedded BI for investigating patterns and reporting; Cohere delivers private LLMs, embeddings and retrieval services to power similarity search, identity resolution and explainability; Dashworks unifies disparate data sources for rapid investigations; Skit.ai enables omnichannel conversational automation for dispute intake and collections; Notion often serves as the knowledge, runbook and case‑management layer. Adopters should evaluate model governance, explainability, data lineage, integration with payment rails and dispute workflows, plus privacy and regulatory controls. The practical trend is toward modular stacks that combine specialist fraud engines with enterprise AI platforms and analytics to achieve measurable reductions in chargeback rates and faster, auditable resolutions.