Author
Listed:
- Marco Corazza
(University of Ca’ Foscari [Venice, Italy])
- René Garcia
(TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, UQAM - Université du Québec à Montréal = University of Québec in Montréal)
- Faisal Shah Khan
(SKEMA Business School Raleigh, USA)
- Davide La Torre
(SKEMA Business School - SKEMA Business School)
- Hatem Masri
(Applied Science University, Bahrain)
Abstract
We wrote this book to help financial experts and investors to understand the state of the art of artificial intelligence and machine learning in finance. But first, what is artificial intelligence? The foundations of artificial intelligence lie in the human desire to automate. Often this desire has had foundations in grand civilization-defining visions or economic needs, such as the Antikythera mechanism, circa 200 BCE. Considered to be the oldest known example of an analog computer, it is thought that the mechanism automated the prediction of the positions of the sun, the moon, and the planets to assist in navigation. No matter the specific industry or application, AI has become a new engine of growth. Both finance and banking have been leveraging AI technologies and algorithms, applying them to automate routine tasks, procedures and forecasting, thereby improving overall customer experience. The topics covered in this book make it an invaluable resource for academics, researchers, policymakers, and practitioners alike who want to understand how AI has affected the banking and financial industries and how it will continue to change them in the years to come.
Suggested Citation
Marco Corazza & René Garcia & Faisal Shah Khan & Davide La Torre & Hatem Masri, 2024.
"Artificial Intelligence and Beyond for Finance,"
Post-Print
hal-04931239, HAL.
Handle:
RePEc:hal:journl:hal-04931239
DOI: 10.1142/q0449
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