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Enterprise AI Strategy 2025: Selecting the Right LLM for FinTech Workflows { "@context": "https://schema.org", "@type": "TechArticle", "headline": "Enterprise AI Strategy 2025: Selecting the Right LLM for FinTech Workflows", "description": "A deep dive into model selection, compliance, cost, latency and platform orchestration for FinTech leaders in 2025.", "author": { "@type": "Person", "name": "Senior Technology Journalist" }, "datePublished": "2025-10-26", "mainEntityOfPage": "https://example.com/enterprise-ai-strategy-2025-fintech" } Enterprise AI Strategy 2025: Selecting the Right LLM for FinTech Workflows In a year where every dollar invested in artificial intelligence is scrutinized against regulatory requirements, latency targets, and return on investment, the decision to adopt a large language model (LLM) can no longer be treated as a purely technical exercise. 2025 has shifted from “pick the best‑selling model” to “architect a portfolio of models that align with your business processes, compliance posture, and cost structure.” Below is a data‑driven framework for FinTech leaders, product managers, and CIOs who must decide which LLM—MiniMax M2, GPT‑4o, GPT‑5 or alternatives—will deliver the greatest strategic advantage. Executive Summary MiniMax M2 offers a 5‑percentage‑point accuracy edge over GPT‑4o across mixed‑domain workloads while matching latency and pricing in most enterprise scenarios. GPT‑5’s modular architecture allows dynamic routing to specialized variants, providing granular cost control and built‑in compliance safeguards—critical for regulated FinTech environments. Until MiniMax M2 matures its tool‑calling capabilities, GPT‑4o remains the safest default for production APIs that require robust external integrations. Private‑cloud deployments are becoming a non‑negotiable compliance requirement; vendors offering turnkey on‑prem or hybrid solutions (Azure OpenAI Service, AWS Bedrock) will capture the largest share of enterprise spend. Enterprise