The Role of AI in Enhancing Financial Decision-Making in Commerce

Abstract

Financial decisions in commerce—whom to lend to, what to stock and at what price, when to hedge, which transactions to flag—have always been made under uncertainty, time pressure and cognitive limitation. Artificial intelligence (AI) alters this situation by sharply reducing the cost of prediction and by extracting signals from data that were previously unusable, such as text, digital footprints and high-frequency transactions. This paper examines how AI enhances financial decision-making in commercial settings and under what conditions that enhancement is realised. Drawing on an integrative review of the finance, information systems and decision-science literatures, we map AI applications across five decision areas: credit and lending, investment and forecasting, fraud detection and audit, pricing and working-capital management, and personal financial advice. We then develop the AI-Augmented Financial Decision Framework (AFDF), which links data, intelligence, decision and outcome layers, and distinguishes three roles for AI—automate, augment and advise—according to the reversibility, frequency and ambiguity of the decision. Human and organisational moderators (calibrated trust, literacy and AI capability) and governance mechanisms (explainability, model risk management and fairness audit) condition the link between algorithmic intelligence and decision outcomes. Six propositions and a governance checklist are offered. The paper argues that AI improves financial decisions not by replacing judgement but by reallocating it, and it outlines implications for enterprises, regulators and educators, with particular attention to small businesses in emerging economies.