《一切都与数据有关:如何在信息泛滥的世界中做出正确的决定

Mehrzad Mahdavi, Hossein Kazemi
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摘要

大数据和替代数据的兴起为金融领域创造了重要的新商机。随着我们开始快速发展的技术颠覆之旅,金融专业人士有一个难得的机会来平衡人工智能(AI)/数据科学的指数级增长与道德、偏见和隐私,以创建可信的数据驱动决策。在本文中,作者讨论了大数据集的细微差别,当人们考虑人工智能系统部署的标准、流程、最佳实践和建模算法时,这些差别是至关重要的。此外,该行业普遍遵循将客户利益置于一切之上的信托标准。因此,彻底了解我们知识的局限性是至关重要的,因为有许多已知的未知和未知的未知会对结果产生重大影响。作者强调了部署人工智能计划的关键成功因素:人才和弥合技能差距。为了实现大数据计划的持久影响,需要建立具有明确角色的多学科团队,并进行持续培训和教育。奖品是未来的金融。•金融领域替代数据的兴起正在金融行业的各个领域创造重大机遇,包括风险管理、投资组合构建、投资银行和保险。•为了在AI/ML计划中建立可信的结果,金融专业人士的角色至关重要。考虑到使用大数据的许多细微差别,在选择数据集和算法时需要经过审查的协议和方法。最佳实践和指导方针可以有效降低使用AI/ML的风险,包括过度拟合、缺乏可解释性、有偏见的输入和不道德的数据使用。•鉴于金融领域人工智能/数据科学人才的严重短缺,员工的实践培训和继续教育是扩大规模以实现金融未来的关键。
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It’s All About Data: How to Make Good Decisions in a World Awash with Information
The rise of big and alternative data has created significant new business opportunities in the financial sector. As we start on this journey of fast-moving technology disruption, financial professionals have a rare opportunity to balance the exponential growth of artificial intelligence (AI)/data science with ethics, bias, and privacy to create trusted data-driven decision making. In this article, the authors discuss the nuances of big data sets that are critical when one considers standards, processes, best practices, and modeling algorithms for the deployment of AI systems. In addition, this industry is widely guided by a fiduciary standard that puts the interests of the client above all else. It is therefore critical to have a thorough understanding of the limitations of our knowledge, because there are many known unknowns and unknown unknowns that can have a significant impact on outcomes. The authors emphasize key success factors for the deployment of AI initiatives: talent and bridging the skills gap. To achieve a lasting impact of big data initiatives, multidisciplinary teams with well-defined roles need to be established with continuing training and education. The prize is the finance of the future. TOPICS: Simulations, big data/machine learning Key Findings • The rise of alternative data in finance is creating major opportunities in all areas of the financial industry, including risk management, portfolio construction, investment banking, and insurance. • To build trusted outcomes in AI/ML initiatives, financial professionals’ roles are critical. Given the many nuances in using big data, there is a need for vetted protocols and methods in selecting data sets and algorithms. Best practices and guidelines are effective in reducing the risks of using AI/ML, including overfitting, lack of interpretability, biased inputs, and unethical use of data. • Given the major shortage of talent in AI/data science in finance, practical training of employees and continued education are keys to scale roll out to enable future of finance.
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