APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE SECTOR OF INVESTMENT FUNDS

undergraduate thesis

• Title (croatian): PRIMJENA UMJETNE INTELIGENCIJE U SEKTORU INVESTICIJSKIH FONDOVA
• Author: Vedran Brozović
• Mentor: Jakša Krišto (mentor)
• Granter: University of Zagreb; Faculty of Economics and Business; (Department of Finance); Zagreb
• Defense date and country: 2019-09-05, Croatia
• Scientific / art field, discipline and subdiscipline: SOCIAL SCIENCES; Economics; Finance

Abstract

Artificial intelligence is neither a new concept nor a completely new research topic. However, advances in data availability and accessibility of high computing power - combined with important achievements in the development of machine learning algorithms - have enabled an exponential growth of the field. By implementing artificial intelligence, investment fund management companies could see major benefits such as cost reduction and improved decision making across all of their departments. The main obstacles in leveraging artificial intelligence are data quality, lack of skilled personnel and maturity of existing technology infrastructure. In order to enable implementation of artificial intelligence in the investment funds sector, issues of auditability of machine learning models, bias of goal-directed models as well as potential implications for financial stability have to be properly addressed and regulated. Regulatory agency should aim to foster financial innovation, safeguard consumers and provide a simple and understandable regulatory framework at the same time.

• Keywords: artificial intelligence; machine learning; investment funds; FinTech
• Language: english
• URN:NBN: urn:nbn:hr:148:811346
• Study programme: Title: Business Economics in English; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica(baccalaureus/ baccalaurea) ekonomije (sveučilišni/a prvostupnik/ prvostupnica(baccalaureus/ baccalaurea) ekonomije)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2019-09-05 13:49:18