Skip to main content
SpringerLink
Log in

The Financial Sphere in the Era of Covid-19: Trends and Perspectives of Artificial Intelligence

  • Chapter
  • First Online:
Finance, Law, and the Crisis of COVID-19

Part of the book series: Contributions to Management Science ((MANAGEMENT SC.))

Abstract

This paper has set itself the objective of studying the importance and the solutions proposed by these artificial intelligence tools to financial decision-making in times of crisis. To meet our objective, we proceeded with a review and schematization of Artificial Intelligence (AI) to address the impacts of COVID on financial markets and corporate financial decisions. In sum, regarding financial decisions perspectives, the more AI investments are amplified, the more low-cost, innovative, and effective solutions can be adopted. However, replacing managers’ knowledge and expertise is still a challenging task. For that, the management team and AI experts’ collaboration is required to soften the digital transformation, survey the market variations, and implement effective solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Abhimanyu, S., et al. (2020). Artificial intelligence and COVID-19: A multidisciplinary approach. Integrative Medicine Research, 9(3), 111.

    Google Scholar 

  • Accenture. (2017). Étude annuelle « Vision technologique ».

    Google Scholar 

  • Agbehadji, I., et al. (2020). Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing. International Journal of Environmental Research and Public Health, 17(15), 1–16.

    Article  Google Scholar 

  • Agrawal, A. K., et al. (2018) Exploring the impact of artificial intelligence: Prediction versus judgment (No. 24626), NBER Working Paper, NBER Working Paper, Cambridge, MA.

    Google Scholar 

  • Allam, Z., et al. (2020). On the coronavirus (COVID-19) outbreak and the smart city network: Universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management. Healthcare, 8, 46–54.

    Article  Google Scholar 

  • Alsuliman, T., et al. (2020). Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality? Curr res Transl me. https://doi.org/10.1016/j.retram.2020.01.002InPress.

  • Amabile, T. (2019)Creativity, Artificial intelligence, and a world of surprises, Academy of Management Journal., https://doi.org/10.5465/amd.2019.0075.

  • AMF (2017a) Comportement des traders haute frĂ©quence sur Euronext Paris.

    Google Scholar 

  • AMF. (2017b) MIF 2 : Guide Ă  l’usage des sociĂ©tĂ©s de gestion de porte-feuille.

    Google Scholar 

  • Ardabili, S., et al. (2020). Covid-19 outbreak prediction with machine learning. https://doi.org/10.2139/ssrn.3580188.

  • Association française de la gestion financière (AFG). (2017). La transformation digitale des sociĂ©tĂ©s de portefeuille en SGP3.0.

    Google Scholar 

  • Atkins, O.(2020). Why the Covid-19 pandemic could kick-start digital transformation at scale. The Drum, 18 May. https://bit.ly/3jE3N4c.

  • Babinet, G. (2015). Big Data, penser l’homme et le monde autrement. Le Passeur.

    Google Scholar 

  • Bardhan, I., et al. (2020). Connecting systems, data, and people: A multidisciplinary research roadmap for chronic disease management. Management Information Systems Quarterly, 44(1), 185–200.

    Google Scholar 

  • Bauwens, M. (2015). Uber et Airbnb n’ont rien Ă  voir avec l’économie de partage. Le Monde. http://www.lemonde.fr/entreprises/article/2015/06/25/michel-bauwens-uber-etairbnb-n-ont-rien-a-voir-avec-l-economie-de-partage_4661680_1656994

  • Benny, P. et al. (2020). A proposal for a privacy-preserving bluetooth based contact tracing scheme for Hamagen.

    Google Scholar 

  • Block, J.(2020). COVID-19 puts spotlight on artificial intelligence. https://www.genengnews.com/gen-edge/covid-19-puts-spotlight-on-artificial-intelligence.

  • Brynjolfsson. (2017). The business of artificial intelligence: What it can—And cannot—Do for your organization. Harvard Business Review.

    Google Scholar 

  • Brynjolfsson, E., et al.(2017). The business of artificial intelligence: What it can–and can not–do for your organization. Harvard Business Review.

    Google Scholar 

  • Bughin, J., et al. (2018a). Skill shift: Automation and the future of the workforce.

    Google Scholar 

  • Bughin, J., et al.(2018b). Skill shift: Automation and the future of the workforce.

    Google Scholar 

  • Bullock, J., et al.(2020). Mapping the landscape of artificial intelligence applications against COVID-19. arXiv preprint. https://arxiv.org/abs/2003.11336

  • Canitez, F., et al. (2020). Sustainable urban mobility in Istanbul: Challenges and prospects. Case Studies on Transport Policy. https://doi.org/10.1016/j.cstp.2020.07.005.

  • Challenges. (2017). Fermeture d’agences, suppressions de postes... Ce que prĂ©voit le plan d’économies de la SociĂ©tĂ© GĂ©nĂ©rale Ă  1 mil-liard d’euros.

    Google Scholar 

  • Chang, S., et al.(2020). Modelling transmission and control of the COVID-19 pandemic in Australia.

    Google Scholar 

  • Charington, S. (2018). AI for materials discovery with Greg Mulholland. https://twimlai.com/twiml-talk-148-ai-for-materials-discovery-with-greg-mulholland/.

  • Charrington, S.(2019). Trends in reinforcement learning with Simon Osindero. This Week in Machine Learning and Artificial Intelligence with the TWIML AI Podcast.

    Google Scholar 

  • Chui, M., et al. (2018). Notes from the AI frontier: AI adoption advances, but foundational barriers remain. McKinsey Analytics.

    Google Scholar 

  • Cliff, O., et al. (2018). Investigating spatiotemporal dynamics and synchrony of influenza epidemics in Australia: An agent-based modeling approach. Simulation Modelling Practice and Theory, 87, 412–431.

    Article  Google Scholar 

  • Cowen, T. (2011). The great stagnation: How America ate all the low-hanging fruit of modern history, got sick, and will (eventually) feel better. Dutton.

    Google Scholar 

  • Daugherty, P., et al.(2018). Human + Machine: Reimagining work in the age of AI. Harvard Business Review Press.

    Google Scholar 

  • David, W. (2020). COVIDSafe, Australia’s digital contact tracing app: The legal Issues. SSRN Electronic Journal, 12(10), 1–13.

    Google Scholar 

  • Dietterich, T. G. & Horvitz, E. J. (2015). Rise of concerns about AI : Re-flections and directions. Communications of the ACM, 58(10), 38 et suivantes.

    Google Scholar 

  • Elavarasan, R., et al. (2020). Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic. Science of the Total Environment, 725, 138858–138875.

    Article  Google Scholar 

  • Enterprise DJI. (2020). https://enterprise.dji.com/news/detail/fight-covid-19-with-drones

  • European Commission. (2015). Communication de la Commission au Par-lement europĂ©en, au Conseil, au ComitĂ© Ă©conomique et social europĂ©en et au ComitĂ© des rĂ©gions—StratĂ©gie pour un marchĂ© unique numĂ©rique en Eu-rope, COM(2015) 192 final. http://eur-lex.euro.

  • European Parliament.(2020). Ten technologies to fight coronavirus. https://www.europarl.europa.eu/RegData/etudes/IDAN/2020/641543/EPRS_IDA(2020)641543_EN.pdf.

  • Gao, S., et al. (2020). Mapping County-level mobility pattern changes in the United States in response to COVID-19. SIG SPATIAL Special, 12, 16–26.

    Article  Google Scholar 

  • Gatto, M., et al. (2020). Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. Proceedings of the National Academy of Sciences, 117, 10484–10491. https://doi.org/10.1073/pnas.2004978117

    Article  Google Scholar 

  • Geyer-Klingeberg, J., et al. (2018). Process mining and robotic process automation: A perfect match. Proceedings of the Dissertation Award, Demonstration, and Industrial Track at Business Process Management Sydney, 2018, 124–131.

    Google Scholar 

  • Ghosh, A., Gupta, R., & Misra, A. (2020). Telemedicine for diabetes Care in India during COVID19 pandemic and National Lockdown Period: Guidelines for physicians. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 273–276.

    Article  Google Scholar 

  • Glikson, E., et al. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14, 627–660.

    Google Scholar 

  • Google. (2020). COVID-19 Exposure Notifications System.

    Google Scholar 

  • Gordon, R. J. (2014). The demise of U.S. economic growth: Restatement, rebuttal, and reflections, NBER Working Paper 19895. http://www.nber.org/papers/w19895.

  • GSMA. (2020). The role of AI for impact in the fight against COVID-19. https://bit.ly/3jnw3be

  • Hao, K.(2018). AI in China: How Uber rival Didi Chuxing uses machine learning to revolutionize transportation. MIT Technology Review.

    Google Scholar 

  • Hu, Z., et al.(2020). Artificial intelligence forecasting of covid-19 in China, arXiv:2002.07112.

    Google Scholar 

  • Huang, L., et al. (2020). Serial quantitative chest CT assessment of COVID-19: Deep-learning approach, radiology: Cardiothoracic. Imaging, 2(2), e200075.

    Google Scholar 

  • Ibrain, J., & Salluh, F. (2020). A COVID-19 dashboard: Data analytics to aid resource allocation in intensive care units. ICU Management & Practice, 20(1), 36–37.

    Google Scholar 

  • Insights, CB. (2019). Artificial intelligence trends. CB Insights.

    Google Scholar 

  • Javaid, M., et al. (2020a). Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14, 419–422.

    Article  Google Scholar 

  • Javaid, M., et al. (2020b). Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 419–422.

    Article  Google Scholar 

  • Johansen, N., et al. (2018). Scaling: A tool for alignment, integration and rare cell identification from scRNA-seq data. Biorxiv.

    Google Scholar 

  • Jones, J. C. (2016). Peering into the future of innovation management. Research Management, 59, 49–58.

    Google Scholar 

  • Karvekar, S.(2019). Smartphone-based human fatigue detection in an industrial environment using gait analysis. https://scholarworks.rit.edu

  • Kowalski, W. (2015). The European digital agenda: Unambitious and too narrow. Social Europe, 6 Juillet 2015. http://www.socialeurope.eu/2015/07/european-digitalagenda-unambitious-narrow.

  • Kumar, A., et al. (2020). A review of modern technologies for tackling COVID-19 pandemic. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14, 569–573.

    Article  Google Scholar 

  • Lai, C., et al. (2020). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. International Journal of Antimicrobial Agents, 55, 105924–105932.

    Article  Google Scholar 

  • Le Monde Économie. (2017). Le CrĂ©dit mutuel dĂ©ploie le robot d’intelligence artificielle Watson dans son rĂ©seau.

    Google Scholar 

  • Lengnick-Hall, C. (1992). Innovation and competitive advantage: What we know and what we need to learn. Journal of Management, 18, 399–429.

    Article  Google Scholar 

  • Lin, K., et al.(2018). Efficient large-scale fleet management via multi-agent deep reinforcement learning. arXiv:1802.06444, 10.

    Google Scholar 

  • Liu, K., et al.(2020). Digital innovation and transformation to business ecosystems. Enterprise Information Systems: 22th International Conference, ICEIS 2020. Springer Nature Switzerland AG.

    Google Scholar 

  • Magar, R., et al.(2020). Potential neutralizing antibodies discovered for novel corona virus using machine learning. arXiv preprint. https://arxiv.org/abs/2003.08447.

  • Maghdid, H., et al.(2020). A novel AI-enabled framework to diagnose coronavirus Covid-19 using smartphone embedded sensors: Design study. arXiv:2003.07434.

    Google Scholar 

  • Mamoshina, P., et al. (2016). Applications of deep learning in biomedicine. Molecular Pharmaceutics, 13, 1445–1454.

    Article  Google Scholar 

  • Manyika, J., et al. (2013). Disruptive technologies: Advances that will transform life, business, and the global economy. McKinsey Global Institute.

    Google Scholar 

  • Mei, X., et al. (2020). Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nature Medicine. https://doi.org/10.1038/s41591-020-0931-3

  • Morariu, C., et al. (2020). Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems. Computers in Industry, 120, 103244.

    Article  Google Scholar 

  • Mueller, J. (2019). The ABCs of assured autonomy. IEEE International Symposium on Technology and Society (ISTAS), 2019, 1–5.

    Google Scholar 

  • NaudĂ©, W.(2020). Artificial intelligence against COVID-19: An early review. IZA Discussion Papers No. 13110. http://ftp.iza.org/dp13110.pdf.

  • Ng, A. (2017). Artificial intelligence is the new electricity.

    Google Scholar 

  • Nguyen, T. (2020). Artificial intelligence in the battle against coronavirus (COVID-19): A survey and future research directions. Preprint. https://doi.org/10.13140/RG.2.2.36491.23846.

  • NHS. (2020). National Health Service Coronavirus (COVID-19).

    Google Scholar 

  • O’Cass, A., et al. (2018). Contemporary issues and critical challenges on innovation in services. Journal of Product Innovation Management, 35, 674–681.

    Article  Google Scholar 

  • O’Halloran, J. (2020). Nearly half of firms to increase investments in iot despite the impact of COVID-19. https://www.computerweekly.com/news/252491333/Nearly-half-offirms-to-increase-investments-in-IoT-despite-the-impact-of-Covid-19

  • OECD.AI. (2020). AI-powered COVID-19 watch. https://www.oecd.ai/covid

  • Open Society Foundation. (2015). Technology and the future of work: The state of the debate. https://www.opensocietyfoundations.org/publications/ technology-and-future-work-state-debate.

  • Petropoulos, G.(2020, March 23). Artificial intelligence in the fight against COVID-19, Bruegel. https://www.bruegel.org/2020/03/artificialintelligence-in-the-fight-against-covid-19/

  • Pinto, R. (2019). Digital sovereignty or digital colonialism?. Sur International Journal on Human Rights, 27.

    Google Scholar 

  • Porter, M. S. (2001). Innovation: Location matters. MIT Sloan Management Review, 42, 28–36.

    Google Scholar 

  • Qin, Z., et al.(2019). Deep reinforcement learning with applications in transportation. 33rd AAAI Conference on Artificial Intelligence.

    Google Scholar 

  • Raju, V., et al.. (2020). Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337–339–125.

    Google Scholar 

  • Ransbotham, S., et al. (2017). Reshaping business with artificial intelligence. MIT Sloan Management Review, 59, 1–17.

    Google Scholar 

  • Rhodes, M. (2016). Airbus’ newest design is based on bones and slime mold.

    Google Scholar 

  • Rifkin, J. (2014). La nouvelle sociĂ©tĂ© du coĂ»t marginal zĂ©ro : l’internet des objets, l’émergence des communaux collaboratifs et l’éclipse du capita-lisme. Editions Les liens qui libèrent.

    Google Scholar 

  • Roldán, J., et al. (2019). Fatigue detection during the sit-to-stand test based on surface electromyography and acceleration: A case study. Sensors, 19, 4202.

    Article  Google Scholar 

  • Roose, K. (2019). The hidden automation agenda of the Davos elite. New York Times B1.

    Google Scholar 

  • Schuhmacher, A., et al. (2020). The upside of being a digital pharma player. Drug Discovery Today, 25(9), 1569–1574.

    Article  Google Scholar 

  • Schumpeter, J. A. (1942). Capitalism, socialism and democracy. Routledge.

    Google Scholar 

  • Sera, W., et al. (2020). Applications of digital technology in COVID-19 pandemic planning and response. The Lancet, 2(8), 435–440.

    Google Scholar 

  • Silverman, K.(2020). Tackling COVID-19 requires better governance of AI and other frontier technologies—Here’s why. World Economic Forum. https://www.weforum.org/agenda/2020/05/success-in-emerging-covid-19-crisis-requires-better-governance-of-ai-and-other-frontier-technologies-here-s-why/

  • Singh, R., et al. (2020). Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14, 521–524.

    Article  Google Scholar 

  • Spieth, P. (2014). Business model innovation—State of the art and future challenges for the field. R&D Management, 44, 237–247.

    Article  Google Scholar 

  • Stahie, S. (2020). COVID-19 pandemic increased iot adoption, research finds. https://www.bitdefender.com/box/blog/iot-news/covid-19-pandemic-increased-iot-adoption-research-finds/.

  • STAT. (2020). Surge in patients overwhelms telehealth services amid coronavirus pandemic. https://www.statnews.com/2020/03/17/telehealth-services-overwhelmed-amid-coronavirus-pandemic/

  • Strzelecki, A.(2020). The Second Worldwide Wave of Interest in Coronavirus since the COVID-19 Outbreaks in South Korea, Italy, and Iran: A Google Trends Study. arXiv preprint. https://arxiv.org/abs/2003.10998

  • Tran, K., et al. (2018). Active learning across intermetallics to guide the discovery of electrocatalysts for CO2 reduction and H2 evolution. Nature Catalysis, 1, 696–703.

    Article  Google Scholar 

  • Veit, F., et al. (2018). The proactive insights engine: Process mining meets machine learning and artificial intelligence. Proceedings of the BPM Demo Track and Business Process Management Dissertation Award, 1920, 1–5.

    Google Scholar 

  • Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4, 404–409.

    Article  Google Scholar 

  • Wang, L., et al. (2020). COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Arvix, 1–14.

    Google Scholar 

  • Wim, N. (2020). Artificial intelligence vs COVID-19: Limitations, constraints and pitfalls. Springer Nature, 23(10), 1–5.

    Google Scholar 

  • Worldometers. (2020). Coronavirus cases. https://www.worldometers.info/coronavirus/

  • Xueyan, M., et al. (2020). Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nature Medicine, 26(10), 1–19.

    Google Scholar 

  • Yang, K., et al. (2019). Machine-learning-guided directed evolution for protein engineering. Nature Methods, 16, 687–694.

    Article  Google Scholar 

  • Yapo, A. (2018). Ethical implications of bias in machine learning. In Proceedings of the 51st Hawaii international conference on system sciences (pp. 5365–5372). Big Island.

    Google Scholar 

  • Ye, Y., et al. (2020). α-Satellite: An AI-driven system and benchmark datasets for hierarchical community-level risk assessment to help combat COVID-19. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2020.3

  • Yun, J., et al. (2019). The role of a business model in market growth: The difference between the converted industry and the emerging industry. Technological Forecasting and Social Change, 146, 534–562.

    Article  Google Scholar 

  • Zachreson, C., et al.(2018). Urbanization affects peak timing, prevalence, and bimodality of influenza pandemics in Australia: Results of a census-calibrated model. Science Advances 4, eaau5294.

    Google Scholar 

  • Zhou, C., et al. (2020). COVID-19: Challenges to GIS with big data. Geography and Sustainability, 1(1), 77–87.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cadi Ayyad University, Marrakesh, Morocco

    Hanane Allioui

  2. ESCA Ecole de Management, Casablanca, Morocco

    Azzeddine Allioui

Authors
  1. Hanane Allioui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Azzeddine Allioui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Azzeddine Allioui .

Editor information

Editors and Affiliations

  1. University of Sousse and University of Salamanca Research Laboratory Larime, Salamanca, Spain

    Nadia Mansour

  2. Facultad de Derecho, University of Salamanca, Salamanca, Spain

    Lorenzo M. Bujosa Vadell

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Allioui, H., Allioui, A. (2022). The Financial Sphere in the Era of Covid-19: Trends and Perspectives of Artificial Intelligence. In: Mansour, N., M. Bujosa Vadell, L. (eds) Finance, Law, and the Crisis of COVID-19. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-030-89416-0_3

Download citation

Publish with us

Policies and ethics

Search

Navigation