It’s All About Data: How to Make Good Decisions in a World Awash with Information
| Autor: | Hossein B. Kazemi, Mehrzad Mahdavi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Information Systems and Management
Knowledge management business.industry Strategy and Management Best practice Big data Investment banking Fiduciary Computational Theory and Mathematics Artificial Intelligence Software deployment Scale (social sciences) Business Management and Accounting (miscellaneous) Business and International Management business Finance Financial services Risk management Information Systems |
| Zdroj: | The Journal of Financial Data Science. 2:8-16 |
| ISSN: | 2640-3943 |
| DOI: | 10.3905/jfds.2020.1.025 |
| Popis: | 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 a key success factor 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. |
| Databáze: | OpenAIRE |
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