{"id":24350,"date":"2026-05-20T09:48:00","date_gmt":"2026-05-20T07:48:00","guid":{"rendered":"https:\/\/www.uni.lu\/snt-en\/?post_type=news&p=24350"},"modified":"2026-05-20T09:48:01","modified_gmt":"2026-05-20T07:48:01","slug":"can-ai-become-a-trustworthy-art-appraiser","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/snt-en\/news\/can-ai-become-a-trustworthy-art-appraiser\/","title":{"rendered":"Can AI become a trustworthy art appraiser?"},"content":{"rendered":"\n
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At University of Luxembourg\u2019s SnT, researcher Afshin Khadankishandi is exploring how AI could make art valuation more transparent and evidence-based. His work looks at how artificial intelligence could help experts, insurers, and investors better assess the value and authenticity of artworks in an increasingly complex market.<\/p>\n\n\n\n

In November 2024, a painting attributed to Andrea del Verrocchio, Leonardo da Vinci’s teacher, sold at Sotheby’s for nearly $10 million. The sale reignited a longstanding debate: in a market where attribution can shift a work’s value by an order of magnitude, how can buyers, insurers, and financiers ever be fully confident in what an artwork is worth? At the latest FutureFinTech Lecture, researcher Afshin Khadankishandi presented one possible answer: auditable, multimodal AI systems designed to support evidence-based appraisal.<\/p>\n\n\n\n

\u201cArt is not only a cultural object\u201d, Afshin explained during the lecture. \u201cIt is also an illiquid asset, a collateral object, an insurance exposure and an alternative investment\u201d.<\/p>\n\n\n

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\nA market in need of infrastructure <\/h2>\n\n\n\n

The global art market generated an estimated $65 billion in sales in 2023, according to the Art Basel and UBS Art Market Report. Yet despite its size, the market remains structurally opaque. Private sales account for a major share of transactions, provenance records are often incomplete, and valuations still rely heavily on specialist judgement that can be difficult to verify externally.<\/p>\n\n\n\n

This is not just a cultural problem. The Deloitte Art and Finance Report<\/a> has repeatedly highlighted the growing role of art as a financial asset used in lending, wealth management and portfolio diversification.<\/p>\n\n\n\n

As artworks increasingly become investment instruments and collateral assets, pressure is growing for more transparent and auditable valuation methods. Afshin\u2019s research aims to address that challenge.<\/p>\n\n\n

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\nWhen authentication fails <\/h2>\n\n\n\n

The risks of poor valuation are not hypothetical. The Beltracchi forgery scandal exposed weaknesses in expert-led authentication after forged paintings attributed to major modern artists sold for millions of euros.<\/p>\n\n\n\n

More recently, the Swiss AI firm Art Recognition made international headlines<\/a> when its algorithms challenged attributions previously accepted as authentic Rembrandts and Caravaggios, raising uncomfortable questions about the reliability of the existing appraisal ecosystem.<\/p>\n\n\n\n

These cases illustrate a point central to Afshin’s lecture: visual analysis alone, whether human or machine, is a weak predictor of value. Provenance depth, social signals and market context matter considerably more.<\/p>\n\n\n\n

In response, Afshin presented a large-scale study analysing 15,000 digitised artworks across 23 artists using large language models to decode stylistic and historical patterns. The system examined:<\/p>\n\n\n\n