AI Agents in Finance

AI Agents in Finance are autonomous software entities—often powered by large language models, predictive analytics, and reinforcement-learning policies—that make or assist in decisions across trading, risk, and customer operations. These agents perceive live market feeds, regulatory data, and client profiles; reason with algorithms for portfolio optimization, fraud detection, or credit scoring; and execute tasks such as placing trades, drafting compliance reports, or negotiating payment plans. Architectures pair a data-ingestion retriever, a decision engine, and a safety layer with human-in-the-loop overrides to meet audit requirements like MiFID II and SOX. Key metrics include Sharpe ratio uplift, false-positive reduction, and latency under 50 ms for market orders. Challenges—model drift, adversarial manipulation, and explainability—are mitigated through LLMOps pipelines, scenario stress tests, and XAI dashboards. By automating high-volume, high-stakes workflows, AI Agents in Finance cut costs, uncover alpha, and elevate customer service while maintaining regulatory compliance.