Recent Advances in Artificial Intelligence for Management and Financial Technology

Renwei Yang; Yun Wang; Yongcan Luo; Zhengjie Yang; Zhimin Zong; Dapeng Oliver Wu

doi:10.53941/tai.2025.100009

Abstract

In this survey, we examine contemporary advancements in Artificial Intelligence (AI) applications for Financial Technology (FinTech), with a specific focus on three rapidly evolving domains: recommendation systems, risk analysis, and AI-generated commercial content (AIGC). For recommendation systems, self-supervised learning and graph neural network methodologies facilitate real-time, hyper-personalized financial product suggestions, optimizing the balance between conversion efficacy and regulatory adherence. For risk analysis, large language models, including GPT-4 and Llama 3, enhanced through sophisticated prompt engineering techniques, have significantly transformed credit assessment and stress testing processes for small and medium-sized enterprises, reducing analytical cycles from weeks to minutes. Concurrently, multimodal generative models, such as DALL-E 3, are revolutionizing advertising through the automated generation of compliant and engaging content across textual, visual, and video formats, markedly compressing production timelines. The survey further critically addresses persistent challenges, encompassing data privacy, algorithmic transparency, and cultural bias within AIGC, while delineating future research trajectories for developing trustworthy and scalable AI solutions in FinTech.

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