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SETSCI - Volume 4 (8) (2019)
ISAS WINTER-2019 (SHS) - 4th International Symposium on Innovative Approaches in Social, Human and Administrative Sciences, Samsun, Turkey, Nov 22, 2019

Financial Machine Learning
Veysel Yılmaz1*
1Tokat Gaziosmanpaşa University, Tokat, Turkey
* Corresponding author: veysel.yilmaz@gop.edu.tr
Published Date: 2019-12-23   |   Page (s): 187-192   |    51     1
https://doi.org/10.36287/setsci.4.8.035

ABSTRACT Machine learning and artificial intelligence have become an integral part of people's culture, influencing the lives of most people today. Machine learning is a subset of data science that uses statistical models to create insights and predictions. Machines should be fed with data by selecting models in their learning experiences. Data scientists train machine learning models with existing data sets and then apply well-trained models to the real-life situation. Financial services sector is also taking important steps in the learning process of machines. Success in financial machine learning depends on building efficient and good infrastructures, collecting appropriate data sets, and applying the right algorithms. Due to the nature of the business, the use of very large data sets related to transactions such as customers, invoices, money transfers is common in the financial service sector. With the development of technology, it is difficult to imagine the future of financial services without machine learning. Despite the difficulties, many financial companies take machine learning very seriously in the execution of financial services. There are several reasons for this. These; reduced operating costs, increased revenue, better compliance, time savings and enhanced security. At the same time, machine learning enables companies to optimize costs, improve customer experience and scale services. In this study, the importance of machines in the provision of financial services, the future of finance, applications and how they are used will be discussed.
KEYWORDS Machine Learning, Financial Markets, Financial Institutions, Financial Services Sector, ATM
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