Open Access
Financial Machine Learning
Veysel Yılmaz1*
1Tokat Gaziosmanpaşa University, Tokat, Turkey
* Corresponding author: veysel.yilmaz@gop.edu.tr

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

SETSCI Conference Proceedings, 2019, 11, Page (s): 187-192 , https://doi.org/10.36287/setsci.4.8.035

Published Date: 23 December 2019    | 1049     10

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

References

[1]. Selçuk, Z. (1999). Gelişim ve Öğrenme Eğitim Psikolojisi. Ankara: Nobel Yayın.
[2]. Yu, D.; Deng, L. (2014). Deep Learning: Methods and Applications.15.10.2019 tarihinde https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/DeepLearning-NowPublishing-Vol7-SIG-039.pdf adresinden alındı.
[3]. Lopez de Prado (a), M. (2018). Advances in Financial Machine Learning. New Jersey, USA: John Wiley & Sons, Inc.
[4]. Ryll, L., & Seidens, S. (2019). Evaluating the Performance of Machine Learning. 10 15, 2019 tarihinde arxiv.org: https://arxiv.org/pdf/1906.07786.pdf adresinden alındı
[5]. Sadgali, I., Sael, N., & Benabbou, F. (2019). Performance of machine learning techniques in the detection of financial frauds. Procedia Computer Science, 148, 45-54.
[6]. Henrique, B., Sobreiro, V., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226-251. doi:https://doi.org/10.1016/j.eswa.2019.01.012
[7]. Erdem, E. (2010). Para Banka ve Finasal Sistem. Ankara: Detay Yayıncılık, Genişletilmiş 3. Baskı.
[8]. Öztürk, N. (2014). Para Banka Kredi. Bursa: Ekin Yayın Dağıtım, Güncellenmiş 2. Baskı.
[9]. Sermaye Piyasası Kanunu. (2012, 12 30). Resmi gazete, 53(28513). Ankara: T.C. Başbakanlık Matbası.
[10]. Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87-106. doi:https://doi.org/10.1257/jep.31.2.87
[11]. Witten , I., Frank , E., & Hall, M. (2011). Data Mining Practical Machine Learning Tools and Techniques. Burlington, USA: Morgan Kaufmann Publishers.
[12]. Witten, I., Frank, E., Hall, M., & Pal, C. (2017). Moving on: applications and beyond. I. H. Witten, E. Frank, M. A. Hall, & C. J. Pal içinde, Data Mining:Practical Machine Learning Tools and Techniques ( 4th Edition) (s. 503-532). Morgan Kaufmann. doi:https://doi.org/10.1016/C2015-0-02071-8
[13]. El Naqa, I., & Murphy, M. (2015). What Is Machine Learning? I. El Naqa, R. Li, & M. Murphy içinde, Machine Learning in Radiation Oncology (s. 3-11). Switzerland: Springer International Publishing. doi:10.1007/978-3-319-18305-3
[14]. Cheung, K. (2019). algorithmxlab. 18. 10. 2019 tarihinde algorithmxlab: https://algorithmxlab.com/blog/applications-machine-learning-finance/ adresinden alındı
[15]. Hargreaves, C. A., Reddy, V. C., & Reddy, R. V. (2017). Machine Learning Application In The Financial Markets Industry. ndian Journal of Scientific Research, 17(1), 253-256.
[16]. Bethapudi, P., Murthy, G., Ashok, P., Prithvi, B., & Kira, S. (2018). ATM Card Fraud Detection System Using Machine Learning Techniques. International Journal of Research, 5(12), 4010-4016.
[17]. López de Prado, M. (2019, 09 18). Beyond Econometrics: A Roadmap Towards Financial Machine Learning. 10 18, 2019 tarihinde https://ssrn.com/abstract=3365282 adresinden alındı
[18]. López de Prado (b), M. (2018). Ten Applications of Financial Machine Learning (Lecture materials). 10 15, 2019 tarihinde https://ssrn.com/abstract=3197726 adresinden alındı.

SETSCI 2024
info@set-science.com
Copyright © 2024 SETECH
Tokat Technology Development Zone Gaziosmanpaşa University Taşlıçiftlik Campus, 60240 TOKAT-TÜRKİYE