![\"\"](\"https:\/\/blog.metu.edu.tr\/alevatak\/files\/2021\/07\/Deep-Learning-in-Finance-300x169.jpg\")
<\/span><\/p>\nIs Deep Learning now leading the charge in terms of financial technology development? For several years, computational economics, artificial intelligence, and deep learning have become crucial parts of the finance industry. The advancement of these strategies, technology, and skills has allowed the financial industry to grow at a breakneck pace over the years, becoming more effective, competitive, and profitable for its participants. Will this prove to be the driving force behind the finance industry’s future?<\/p>\n
Uses of Deep Learning in Finance<\/b><\/p>\n
Deep Learning is a concept that deals with complex inputs and generates outputs dependent on them. It also has the ability to self-correct, and it is built to be robust enough not to require human interference. As a consequence, after data is entered, this device develops from its successes and mistakes. There are many prominent places in the finance world where AI, or more precisely, Deep Learning, can be used. So, let’s take a look at some of the main places where Deep Learning is used:<\/p>\n
Stock Market Prediction<\/b><\/p>\n
The neural networks in Deep Learning forecast stock prices based on historical data and various parameters of the current market trend. The forecast quality increases as Deep Learning utilizes historical results in greater depth, including the hidden layers. As a consequence, it is evident that Deep Learning has the highest predictive performance.<\/p>\n
Automation of Process<\/b><\/p>\n
This system aids operations by automating contact centers, document processing, and gratifying staff preparation, among other things.<\/p>\n
Analyzing Trading Strategies<\/b><\/p>\n
Deep Learning algorithms are much simpler than humans because they can evaluate thousands of data points at once. The trading techniques established as a result of this study are much more efficient.<\/p>\n
Financial Security<\/b><\/p>\n
The rise in internet purchases has also resulted in a spike in the number of illegal activities. Deep Learning algorithms are very effective at identifying frauds, so financial stability is done at the same time.<\/p>\n
Robo Advisory<\/b><\/p>\n
The algorithms used for providing advice on financial products are referred to as Robo-advisory. For example, to promote financial products such as insurance and to monitor funds through different investment opportunities.<\/p>\n
Credit Card Customer Research<\/b><\/p>\n
Since banks rely on their clients using their credit cards, the Deep Learning technology assists in identifying those clients. As a result, the framework includes more intelligent questions to be included on credit card applications in order to recognize the correct consumers.\u00a0<\/span><\/p>\n It has been covered most of the fields where Deep Learning has proved to be helpful. However, there are plenty more, including credit acceptance, business failure prediction, and bank fraud, to name a few.<\/p>\n Source<\/b><\/p>\n Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications : A survey. Applied Soft Computing<\/i>, 93<\/i>, 106384. https:\/\/doi.org\/10.1016\/j.asoc.2020.106384<\/span><\/a>\u00a0<\/span><\/p>\n Huang, J., Chai, J., & Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China<\/i>, 14<\/i>(1), 1\u201314. https:\/\/doi.org\/10.1186\/s11782-020-00082-6<\/span><\/a>\u00a0<\/span><\/p>\n