Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1272))

  • 771 Accesses

Abstract

Banking operations have been supported by latest technology-based systems along with the traditional processes which prevailed in earlier banking era. With the large number of smart phone users, there has been a paradigm shift in increasing demand for digital products like mobile banking, Internet banking, E-wallets, etc. A common trend in banking technology is using an application programming interface (API) that enables a third-party application to use an interface through which the customer can access a variety of services. Besides APIs, technologies like blockchain and artificial intelligence (AI) have great impact in changing the face of banking industry. However, this exposes the banks to a variety of cybersecurity threats which may cause service disruptions to the customers. Therefore, this study discusses the pros and cons of these AI techniques along with their scope of application for banks’ asset management.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** 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

  1. Jaksic, M., Marinc, M.: Relationship banking and information technology: the role of artificial intelligence and FinTech. Risk Manag. 21(1), 1–18 (2019)

    Article  Google Scholar 

  2. Kishada, Z.M., Wahab, N.A., Mustapha, A.: Customer loyalty assessment in Malaysian Islamic banking using artificial intelligence. J. Theor. Appl. Inf. Technol. 87(1), 80–91 (2016)

    Google Scholar 

  3. Eletter, S.F., Yaseen, S.G.: Applying neural networks for loan decisions in the Jordanian commercial banking system. Int. J. Comput. Sci. Netw. Secur. 10(1), 209–214 (2010)

    Google Scholar 

  4. Milkau, U., Bott, J.: Active management of operational risk in the regimes of the “Unknown”: what can machine learning or heuristics deliver? Risks 6, 2 (2018)

    Article  Google Scholar 

  5. Elizabeth, M.P., Peltier, J.W., Barger, V.A.: Mobile banking and AI-enabled mobile banking: an international journal. J. Res. Inter. Market. 12(3), 328–346 (2018)

    Google Scholar 

  6. Ammirato, S., Sofo, F., Felicetti, A.M., Raso, C.: A methodology to support the adoption of IoT innovation and its application to the Italian bank branch security context. Eur. J. Innov. Manag. 22(1), 146 (2019)

    Article  Google Scholar 

  7. Kumar, K.N., Balaramachandran, P.R.: Robotic process automation—a study of the impact on customer experience in retail banking industry. J. Internet Bank. Comm. 23(3), 1–27 (2018)

    Google Scholar 

  8. Rar, T.: Scopes of machine learning and artificial intelligence in banking & financial services | ML & AI—The Future of Fintechs (2017). [Online https://www.stoodnt.com/blog/scopes-of-machine-learning-and-artificial-intelligence-in-banking-financial-services-ml-ai-the-future-of-fintechs/]

  9. Duygun-Fethi, M., Jackson, G.: Assessing bank performance with operational research and artificial intelligence techniques: a survey, Working Paper Series (2009.02). http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=9DB0586DD7C1955356A60E75A477FF23?doi=10.1.1.473.4002&rep=rep1&type=pdf

  10. Ince, H., Aktan, B.: A comparison of data mining techniques for credit scoring in banking: a managerial perspective. J. Bus. Econ. Manag. 10(3), 233–240 (2009)

    Article  Google Scholar 

  11. Moro, S., Cortez, P., Rita, P.: Business intelligence in banking: a literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Syst. Appl. 42(3), 1314–1324 (2015)

    Article  Google Scholar 

  12. Kouatli, I.: A comparative study of the evolution of vulnerabilities in IT system and its relation to the new concept of cloud computing. J. Manag. Hist. 20(4), 409–433 (2014)

    Google Scholar 

  13. Souri, A., Asghari, P., Rezaei, R.: Software as a service based CRM providers in the cloud computing: challenges and technical issues. J. Serv. Sci. Res. 9(2), 219–237 (2017)

    Article  Google Scholar 

  14. Lacheheub, M.N., Maamri, R.: Pr. Towards a construction of an intelligent business process based on cloud services and driven by degree of similarity and QoS. Inf. Syst. Front. 18(6), 1085–1102 (2016)

    Article  Google Scholar 

  15. Mourtzis, D., Vlachou, E.: Cloud-based cyber-physical systems and quality of services. TQM J. 28(5), 704–733 (2016)

    Article  Google Scholar 

  16. Pandeeswari, N., Kumar, G.: Anomaly detection system in cloud environment using fuzzy clustering based ANN. Mobile Netw. Appl. 21(3), 494–505 (2016)

    Article  Google Scholar 

  17. Park, J., An, Y., Kang, T., Yeom, K.: Virtual cloud bank: Consumer-centric service recommendation process and architectural perspective for cloud service brokers. Comput. Arch. Inf. Numer. Comput. 98(11), 1153–1184 (2016)

    MathSciNet  Google Scholar 

  18. Charlo, M.J.: The most relevant variables to support risk analysts for loan decisions: an empirical study. Reg. Sect. Econ. Stud. 10(1), 61–70 (2010)

    Google Scholar 

  19. Metawa, N., Hassan, M.K., Elhoseny, M.: Genetic algorithm based model for optimizing bank lending decisions. Expert Syst. Appl. 80, 75–82 (2017)

    Article  Google Scholar 

  20. Berendt, B., Preibusch, S.: Better decision support through exploratory discrimination-aware data mining: foundations and empirical evidence. Artif. Intell. Law. 22(2), 175–209 (2014)

    Article  Google Scholar 

  21. Hamid, A.J., Ahmed, T.M.: Develo** prediction model of loan risk in banks using data mining. Mach. Learn. Appl. Int. J. (MLAIJ). 3(1), 1–9 (2016)

    Google Scholar 

  22. Moonasar, V.: Credit risk analysis using artificial intelligence: evidence from a leading South African Banking Institution. Doctoral dissertation, University of South Africa (2007). https://s3.amazonaws.com/academia.edu.documents/3456049/Credit_risk_analysis_using_artificial_intelligence_evidence_from_a_leading_South_African_banking_institution

  23. Abubakar, A.M.: Using hybrid SEM—artificial intelligence. Personnel Review 49(1), 67–86 (2019)

    Article  Google Scholar 

  24. Caron, M.S.: The transformative effect of AI on the banking industry. Bank. Finance Law Rev. 34(2), 169–214 (2019)

    Google Scholar 

  25. Asadi, S., Nilashi, M., Abd Razak, C.H., Yadegaridehkordi, E.: Customers perspectives on adoption of cloud computing in banking sector. Inf. Technol. Manage. 18(4), 305–330 (2017)

    Article  Google Scholar 

  26. Cearnău, D.: Block-cloud: The new paradigm of cloud computing. Acad. Econ. Stud. Econ. Informat. 19(1), 14–22 (2019)

    Google Scholar 

  27. Ghane, F., Gilaninia, S., Homayounfar, M.: The effect of cloud computing on effectiveness of customer relation management in electronic banking industry: a case study of Eghtesad Novin Bank. Kuwait Chapter Arab. J. Bus. Manag. Rev. 5(8), 50–61 (2016)

    Article  Google Scholar 

  28. Sarma, A., Girao, J.: Supporting trust and privacy with an identity-enabled architecture. Fut. Internet. 4(4), 1016–1025 (2012)

    Article  Google Scholar 

  29. Dietz, M., HV, V., Lee, G.: Bracing for seven critical changes as fintech matures. Panorma. Mc Kinsey & Co., (2016) [Online: https://www.mckinsey.com/industries/financial-services/our-insights/bracing-for-seven-critical-changes-as-fintech-matures]

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Priya Gupta .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gupta, P., Bhatia, P. (2021). Role of Artificial Intelligence in Bank’s Asset Management. In: Pandian, A.P., Palanisamy, R., Ntalianis, K. (eds) Proceedings of International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1272. Springer, Singapore. https://doi.org/10.1007/978-981-15-8443-5_13

Download citation

Publish with us

Policies and ethics

Navigation