{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T11:53:19Z","timestamp":1791546799207,"version":"4.3.4"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>&lt;p&gt;&lt;span&gt;Artificial intelligence (AI) expands financial prediction and information processing, but technical gains do not necessarily improve allocation. This theory-guided survey synthesizes 209 publications through information economics, constrained choice, delegation, and strategic feedback. We trace tool evolution and distinguish measurement, historical policy evaluation, observed adoption, and equilibrium mechanisms. Three conclusions organize the evidence. First, prediction gains create net value only through feasible actions after implementation costs. Second, more precise signals can improve selection while either expanding or contracting funding; contracts and capacity determine the allocation response. Third, human discretion and counterparty adaptation make deployed performance endogenous. A Gaussian benchmark and five conditional propositions explain these distinctions without claiming a new unified equilibrium. The literature supports task-specific information gains, heterogeneous credit-pricing counterfactuals, and issuer adaptation, while sustained deployment value, competitive rent erosion, and aggregate welfare remain less securely established. The synthesis identifies the assumptions behind apparently conflicting findings and prioritizes empirical designs that observe thresholds, rival access, recommendations, and executed decisions.&lt;\/span&gt;&lt;\/p&gt;<\/jats:p>","DOI":"10.2139\/ssrn.7586640","type":"posted-content","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T11:13:52Z","timestamp":1791544432000},"source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Finance: From Prediction to Economic Value"],"prefix":"10.2139","author":[{"given":"Lai Van","family":"Vo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huong T. T.","family":"Le","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T11:13:52Z","timestamp":1791544432000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7586640"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7586640","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}