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Linedata:2024 AI在资产管理中的应用状况深度剖析报告:案例、资产类别差异与痛点解决策略(中译版)(22页).pdf

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1、 WHATS NEXT?A CLOSER LOOK AT ARTIFICIAL INTELLIGENCE IN ASSET MANAGEMENTMAY 2024There are no signs of the AI buzz abating.To cherry pick recent examples,JP Morgan Chase CEO,Jamie Dimon,has compared AIs potential impact on the global economy to that of electricity.BlackRock CEO,Larry Fink,says AI wil

2、l have tremendous productivity benefits,and could even push wages higher in the future as fewer people are needed to produce more.The list goes on of experts,commentators and industry leaders who have lauded the technologys transformational potential.Still,there is a very important distinction to dr

3、aw between what AI can be and what it currently is a dichotomy that has the capacity to cause significant frustration for many a business.In the world of alternative asset management,expectations for AI have ranged from the futuristic to the fundamental from taking over the trading and investment pr

4、ocess entirely to aiding with incremental process improvement.In truth,most firms are still working to achieve the latter,while a handful have put AI at the heart of their proposition and are tearing ahead with new applications.This report takes a close look across the spectrum of transformation in

5、alternatives,to understand what the real use cases are,how these differ from one asset class to the next,and how firms can overcome some of the most common pain points that stand in the way of progress.In section one,we explore the current state of play,while section two looks at where firms want to

6、 be and how they can get there.Along the way,we hear from experts on the data management problem,the limitations with current products on the market,and how some firms have had success with proprietary tools.The standout conclusion:its important not to rush into AI implementation for the sake of it

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本文主要探讨了人工智能在另类资产管理中的应用现状与未来趋势。文章指出,尽管人工智能技术在另类资产管理中的应用日益增多,但大多数公司仍处于实施和扩展阶段。文章引用了调查数据,显示36%的私募股权和对冲基金公司已经将人工智能/生成式人工智能(AI/GenAI)的使用案例投入生产,其中14%的公司在这方面已经较为先进,并有更多的计划。文章还指出,数据质量是实施和扩展AI解决方案的中心障碍,而成本问题在从实施阶段到扩展阶段的转变中变得尤为突出。文章强调,成功的AI实施需要建立全面的数据战略,并指出,大多数公司需要进行数据整合,将来自不同系统和格式的相关信息汇集到单一的真实来源中。最后,文章指出,AI不应被视为一种可以立即加速转型旅程的银弹,而是一个需要时间来建立正确基础、获得支持、推动文化变革和解决风险因素的科技项目。
人工智能在资产管理的应用现状如何? 数据质量对人工智能实施的影响有哪些? 人工智能在替代资产管理中的应用前景如何?
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