Artificial Intelligence in Finance.

Introduction

If there is one technology that is paying dividends, it is Artificial Intelligence (AI) in Finance. Artificial intelligence has given the world of banking and the financial industry as a whole a way to meet the demands of customers who want smarter, more convenient, safer ways to access, spend, save and invest their money.

AI in finance is transforming the way we interact with money. AI is helping the financial industry to streamline and optimize processes ranging from credit decisions to quantitative trading and financial risk management.

There are different interest application areas includuing portfolio of stocks management and forecast. Also AI solutions are helping banks and credit lenders make smarter underwriting decisions by utilizing a variety of factors that more accurately assess traditionally underserved borrowers, like millennials, in the credit decision making process.

Indeed, estimation for Volatility worth to understand the stock market behavior, which anticipates the coming economic turmoils or events.

Development and application of machine learning software can be useful to different people related to the business, data scientists, business analysts, software engineers, executives and IT professionals.

These techniques can help financial institutions and businesses quickly build accurate predictive models that enhance decision making around issues like fraudulent credit card transactions, digital wealth management, direct marketing, blockchain, lending and more.

Related Publications

Some of the interesting papers produced in this research can be found underneath,

J. N. Franco-Riquelme, A. Bello-Garcia, and J. Ordieres-Meré, “Indicator proposal for measuring regional political support for the electoral process on twitter: the case of spain’s 2015 and 2016 general elections,” Ieee access, vol. 7, p. 62545–62560, 2019.
[Bibtex]

@article{franco2019indicator,
author = {Jose N Franco-Riquelme and Antonio Bello-Garcia and Joaqu\'in Ordieres-Meré},
citations = {17},
doi = {10.1109/ACCESS.2019.2917398},
gsid = {7810204091234512555},
journal = {IEEE Access},
ncites = {14},
note = {\textbf{Q1}; 3.745; Computer Systems, Information Systems},
pages = {62545--62560},
publisher = {IEEE},
title = {Indicator Proposal for Measuring Regional Political Support for the Electoral Process on Twitter: The Case of Spain’s 2015 and 2016 General Elections},
url = {https://ieeexplore.ieee.org/document/8716646},
volume = {7},
year = {2019}
}

Y. Liu, Q. Zeng, J. Ordieres Meré, and H. Yang, “Anticipating stock market of the renowned companies: a knowledge graph approach,” Complexity, vol. 2019, 2019.
[Bibtex]

@article{liu2019anticipating,
author = {Yang Liu and Qingguo Zeng and Joaqu\'in {Ordieres Meré} and Huanrui Yang},
citations = {62},
doi = {10.1155/2019/9202457},
gsid = {11367797857471579809},
journal = {Complexity},
ncites = {58},
note = {\textbf{Q2}; 2.462; Mathematics, Interdisciplinary Applications},
publisher = {Hindawi},
title = {Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach},
url = {https://www.hindawi.com/journals/complexity/2019/9202457/},
volume = {2019},
year = {2019}
}

Y. Liu, H. Fei, Q. Zeng, B. Li, L. Ma, D. Ji, and J. Ordieres Meré, “Electronic word-of-mouth effects on studio performance leveraging attention-based model,” Neural computing and applications, p. 1–22, 2020.
[Bibtex]

@article{liu2020electronic,
author = {Yang Liu and Hao Fei and Qingguo Zeng and Bobo Li and Lili Ma and Donghong Ji and Joaqu\'in {Ordieres Mer\'e}},
citations = {18},
doi = {s00521-020-04937-0},
gsid = {2019731130503885995},
journal = {Neural Computing and Applications},
ncites = {12},
note = {\textbf{Q1}; 4.774; Computer Science, Artificial Intelligence},
pages = {1--22},
publisher = {Springer},
title = {Electronic word-of-mouth effects on studio performance leveraging attention-based model},
url = {https://link.springer.com/article/10.1007/s00521-020-04937-0},
year = {2020}
}

Related Theses

Indeed, it is possible to make reference to specific thesis already finished, which are related to this research line

Y. Liu, “Applications of artificial intelligence in behavioral finance getting benefit from extended data sources.,” Tesis PhD Thesis, 2020.
[Bibtex]

@PHDTHESIS{upm20200623,
title = { Applications of artificial intelligence in behavioral finance getting benefit from extended data sources.},
school = {Industrial Engieering. Universidad Polit\'ecnica de Madrid},
author = {Yang Liu},
year = {2020},
type = {Tesis},
note = {autor, Yang Liu,
Advisors, Joaqu\'in Ordieres Mer\'e},
university = {Universidad Polit\'ecnica de Madrid,
Programa DEGIN,
Departamento de Ingenier\'ia de
Organizaci\'on Administraci\'on de Empresas y Estad\'istica,
Universidad Polit\'ecnica de Madrid}
}

Related Research Projects

Some research projects are aligned to this research line, such as,

Code	URL of the project / Title	Funding source
HFT-1	High frequency trading models	Private