{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:01:41Z","timestamp":1790694101340,"version":"4.1.0"},"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:\/\/www.uspto.gov\/ip-policy\/copyright-policy\/copyright-basics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>The total portfolio approach (TPA) asks asset owners to judge every position by its contribution to total-fund objectives rather than within asset-class silos. That ambition creates an analytical burden that AI systems are increasingly proposed to carry. This letter argues that AI can make TPA operational only if it is embedded in a governed process rather than treated as an autonomous optimizer. We propose a seven-step governed AI process built on executable mandate objects, a firewall and action budgets, and map it onto the TPA decision cycle. We specify three lenses that the process must run on every candidate portfolio: factor exposures, macro-regime scenarios, and holistic tail risk measured by both value-at-risk (VaR) and expected shortfall (ES). A stylized, synthetic illustration shows that candidates scaled to the same 10% volatility differ sharply in factor composition (equity drives 33% to 95% of baseline variance), that a stagflation-type correlation shift raises volatility from 10% to between 11.1% and 11.9% (relative increases of 11.3% to 18.5%), and that ES at 97.5% exceeds VaR at 99% by about 6.6% under fat tails but not under a Gaussian assumption. We close with three testable propositions and a research agenda. The contribution is conceptual; we claim no empirical validation.<\/jats:p>","DOI":"10.2139\/ssrn.7540630","type":"posted-content","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T14:39:29Z","timestamp":1790692769000},"source":"Crossref","is-referenced-by-count":0,"title":["Governed AI for the total portfolio approach: factor, macro and tail-risk lenses"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7061-1961","authenticated-orcid":true,"given":"Matthias","family":"Uhl","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T14:39:29Z","timestamp":1790692769000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7540630"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7540630","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}