Top

Published in:

2024 | OriginalPaper | Chapter

Human Centered AI for Financial Decisions

Author : Polina Khrennikova

Published in: Applications of Optimal Transport to Economics and Related Topics

Publisher: Springer Nature Switzerland

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

We survey the state of the art of AI applications to financial expectations and the role quantum logic can play in further advancements of AI technologies. We discuss financial applications of such machine learning techniques as reinforcement learning and deep neural networks to the analysis of financial statements, algorithmic trading, portfolio management, and robo-advising. Next, we elaborate on the emergence and advancement of QML (quantum machine learning) and advocate for the wider exploration of the advantages of quantum inspired neural networks, steaming from the use of quantum logic that is able to capture agents’ non- classical expectations and non expected utility decisions, also coined “bounded rationality”. We would like to motivate to use human—like AI techniques that are centered on quantum, rather than classical logic to (i) represent the human brain type information processing; (ii) speed up the work of the AI algorithms; (iii) better operate in complex and uncertain environments.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Multivariate Quantiles: Geometric and Measure-Transportation-Based Contours

next chapter Digital Economy, Labor Mobility and Industrial Structure Optimization—Empirical Analysis Based on Mediating Effect and Threshold Effect

Deep neural networks are in a sense an advancement of the earlier algorithms based on artificial neural networks, where the DNNs have a more complex structure, with multiple hidden layers between the input and output layers, aimed at mimicking the human brain information processing, see [30] for detailed discussion.

For an introduction to AI algorithms in finance and investments, see, e.g., the primer by [31].

From: “Banking on the bots: unintended bias in AI”, [38].

The circumstances in which agents switch to non classical information processing is an intriguing question, yet to be fully explored, see [9] and a discussion in the setting of financial markets in [24].

Abbas, A., Sutter, D., Zoufal, C., et al.: The power of quantum neural networks. Nat Comput. Sci. 1, 403–409 (2021)CrossRef

Arute, F., Arya, K., Babbush, R., et al.: Quantum supremacy using a programmable superconducting processor. Nature 574, 505–510 (2019). https://doi.org/10.1038/s41586-019-1666-5

Back, C., Morana, S., Spann, M.: When do robo-advisors make us better investors? The impact of social design elements on investor behavior. J. Behav. Exp. Econ. 103, 101984 (2023)

Barberis, N., Shleifer, A., Vishny, R.: A model of investor sentiment. J. Financ. Econ. 49, 307–343 (1998)

Birkhoff, G., von Neumann, J.: The logic of quantum mechanics. Ann. Math. 37, 823–843 (1936)CrossRef

Biamonte, J., et al.: Quantum machine learning. Nature 549, 195–202 (2017)CrossRef

Boole, G.: An Investigation of the Laws of Thought on Which are Founded the Mathematical Theories of Logic and Probabilities. Macmillan (1958[1854]). Reprinted with corrections, Dover Publications, New York, NY (reissued by Cambridge University Press, 2009)

Bouchard, J.P., Krüger, P., Landier, A., Thesmar, D.: Sticky expectations and the profitability anomaly. J. Financ. 74, 639–674 (2019)CrossRef

Busemeyer, J., Bruza, P.: Quantum Models of Cognition and Decision. Cambridge University Press (2012)

10.

Busemeyer, J., Wang, Z.: What is quantum cognition, and how is it applied to psychology? Curr. Dir. Psychol. Sci. 24(3), 163–169

11.

Daley, A.J., et al.: Practical quantum advantage in quantum simulation. Nature 607, 667–676 (2022)CrossRef

12.

Dr. Varsha, P.S.: How can we manage biases in artificial intelligence systems—A systematic literature review. Int. J. Inf. Manag. Data Insights 3(1), 100–165 (2023)

13.

Gigerenzer, G., Selten, R.: Bounded Rationality: The Adaptive Toolbox. Dahlem Workshop Reports (2001)

14.

Glimcher, P., Fehr, E. (eds.): Neuroeconomics: Decision Making and the Brain. Academic Press, Elsevier (2014)

15.

Hornik, K., Stinchcombe, M.B., White, H.L.: Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359–366 (1989)CrossRef

16.

Huang, H.L., et al.: Experimental quantum generative adversarial networks for image generation. Phys. Rev. Appl. 17, 1013–1017 (2021)

17.

Jiang, H., Zhengzi, L.S., Wang, H.: Pervasive underreaction: evidence from high-frequency data. J. Financ. Econ. 141(2), 573–599 (2021)CrossRef

18.

Kahneman, D.: Thinking. Fast and Slow, Farrar Straus and Giroux (1994)

19.

Khrennikov, A.: Open systems, quantum probability, and logic for quantum-like modeling in biology, cognition, and decision-making. Entropy 25, 886 (2023). https://doi.org/10.3390/e25060886

20.

Khrennikov, A., Basieva, I., Pothos, E.M., et al.: Quantum probability in decision making from quantum information representation of neuronal states. Sci. Rep. 8, 16225 (2018)

21.

Khrennikov, A., Haven, E.: Quantum mechanics and violations of the sure—thing principle: the use of probability interference and other concepts. J. Math. Psychol. 53, 378–388 (2009)CrossRef

22.

Kreps, M.D.: Notes on the Theory of Choice. Westview Press (1988)

23.

Khrennikova, P., Patra, S.: Asset Trading under non-classical ambiguity and heterogeneous beliefs. Phys. Stat. Mech. Appl. 521, 562–577 (2019)CrossRef

24.

Khrennikova, P.: Application of quantum master equation for long term prognosis of asset prices. Phys. Stat. Mech. Appl. 450, 253–263 (2016)CrossRef

25.

Liu, X., Yang, H., Gao, J., Wang, C.: FinRL: deep reinforcement learning framework to automate trading in quantitative finance. In: Proceedings of the Second ACM International Conference on AI (2021). https://doi.org/10.1145/3490354.3494366in Finance

26.

McCulloch, W., Pitts, W.: A logical calculus of ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)CrossRef

27.

Melucci, M.: Introduction to Information Retrieval and Quantum Mechanics. Springer, Berlin (2015)

28.

Melucci, M.: Deriving a quantum information retrieval basis. Comput. J. 56(11), 1279–1291 (2013)CrossRef

29.

Montanaro, A.: Quantum algorithms: an overview. Quantum Inf. 2, 1–8 (2016)

30.

Nielson, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press (2010)

31.

Osterrieder, J., GPT, C.: A Primer on Deep Reinforcement Learning for Finance (January 2, 2023). Available at SSRN: https://ssrn.com/abstract=4316650 or https://doi.org/10.2139/ssrn.4316650

32.

Plotnitsky, A., Haven, E. (eds.) The Quantum -Like Revolution: A Festschrift for Andrei Khrennikov. Springer, Berlin/Heidelberg, Germany, New York, NY, USA (2023)

33.

Shafir, E., Tversky, A.: Thinking through uncertainty: non-consequential reasoning and choice. Cogn. Psychol. 24(4), 449–474 (1992)CrossRef

34.

Simon, H.A.: Behavioral model of rational choice. Q. J. Econ. 69, 99–118 (1955)CrossRef

35.

Von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behaviour, Princeton. Princeton University Press, NJ (1953)

36.

Xiao, T., Zhai, X., Wu, X., et al.: Practical advantage of quantum machine learning in ghost imaging. Commun. Phys. 6, 171 (2023). https://doi.org/10.1038/s42005-023-01290-1

37.

Ying, M.: Quantum computation, quantum theory and AI. Artif. Intell. 174(2), 162–176 (2010)CrossRef

38.

Deloitte UK., Banking on the bots: unintended bias in AI. https://www2.deloitte.com/uk/en/pages/financial-services/articles/banking-on-the-bots-unintended-bias-in-ai.html) (2023). Accessed on 7 Sept 2023

39.

Financial Times: Advance of the AI adviser: machine learning’s role in managing fortunes. https://on.ft.com/3ZfOELI (2023). Accessed on 31 Aug 2023

40.

OECD: Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges, and Implications for Policy Makers. https://www.oecd.org/finance/artificial-intelligence-machine-learningbig-data-in-finance.htm (2021). Accessed on 4 Sept 2023

Title: Human Centered AI for Financial Decisions
Author: Polina Khrennikova
Publisher: Springer Nature Switzerland
Book: Applications of Optimal Transport to Economics and Related Topics
Print ISBN: 978-3-031-67769-4

Electronic ISBN: 978-3-031-67770-0

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-67770-0_7

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"