AI-DRIVEN DECISION SUPPORT FOR ART COLLECTORS - Sharma, Anjali; Jadhav, Manisha Tushar; Jain, Garishma; Goyal, Shweta; Dhanalakshmi V; Ahmed, Gousia (ShodhKosh: Journal of Visual and Performing Arts, 2026)
Global art market is typified by substantial monetary investment, lack of information, and a certain degree of subjectivity, which makes it difficult to make informed choices in the face of collectors. This project aims to introduce an AI-based decision support system that could help the art collector in valuation and authenticity evaluation, as well as in the market risk analysis on the basis of an integrated, data-driven system structure. The suggested system utilizes multimodal data of art, such as the high-resolution images of artwork, provenance information, auction history, annotations of experts and market sentiment indicators, to generate full-fledged and explainable recommendations. The framework consists of an AI-enhanced multi-criteria decision support framework comprising of computer-vision based feature extraction, machine-learning based valuation algorithms, and predictive market forecasting. Deep image embeddings encode stylistic and material features, structured metadata, as well as provenance intelligence help to verify authenticity and estimate the risk of forgery. The models include supervised learning and learning in ensembles that are used to approximate fair market value, price volatility and investment risk among artists in various periods and market segments. Exemplary study shows that the suggested system is more accurate in valuation and less ambiguous in authenticity and increases the transparency of decisions as compared to both the expert only or rule-based system of decision making. In addition to immediate valuation tasks, the framework aids in strategic collection planning, that is, by modeling the future market execution and pointing out the risk-reward trade-offs.