AI-powered finance assistants combine conversational interfaces with direct access to bank, bookkeeping and transactional data to automate tasks like expense categorization, split payments, reconciliations and real‑time advice. This topic examines how ChatGPT Finances and competing fintech assistants deliver those functions while managing the security, consent and governance challenges of account linking. Relevance in 2026 stems from two forces: broader consumer adoption of AI helpers across personal and small‑business finance, and intensified scrutiny over data access and model governance. Secure account linking (tokenized connectors, scoped OAuth, consent logs) is now a baseline requirement; vendors must balance frictionless access with replayable consent, least privilege, and auditable data flows. At the platform level, solutions diverge: specialist tools like Bookeeping.ai (Paula AI) focus on automating bookkeeping workflows for freelancers and small businesses; Splitfair augments bill‑splitting with AI scanning and categorization; enterprise platforms such as Kore.ai enable multi‑agent orchestration with governance and observability; Cohere provides private, customizable LLMs, embeddings and retrieval for organizations that need controllable models; FirstMate integrates AI agents with human architects for ERP implementations; AI Knowledge Search by Amurex surfaces personal and workplace records behind unified, permissioned search. Key comparisons include how assistants authenticate and refresh access, whether models run on private or third‑party LLMs, support for retrieval‑augmented workflows, and enterprise features like observability and policy enforcement. Understanding these tradeoffs—security vs convenience, on‑premise models vs managed LLMs, single‑purpose bots vs multi‑agent platforms—helps buyers and users choose assistants that meet both everyday finance needs and regulatory, privacy and audit requirements.