Skip to main content

I-FinTech and Its Value Proposition for Islamic Asset and Wealth Management

  • Chapter
  • First Online:
Islamic FinTech
  • 721 Accesses

Abstract

Artificial Intelligence (AI) is a highly evolved area of computer science that strives to create intelligent machines that can replicate certain human behaviour without its irrationalities for better predictability and consistency. Advanced AI that utilizes machine learning makes it possible for machines to learn from previous data (experience), adjust to new inputs (instructions) and perform tasks through updated algorithms. Through sophisticated algorithms, modern AI systems can be trained to accomplish specific tasks by processing large amounts of data, obtaining insights and recognizable patterns in the data to act upon. As such AI has become a hot topic, with much interest on its advantages to the highly regulated financial services industry.

Similarly, blockchain technology also has the potential to both enrich and improve financial processes and asset management systems, and progressive corporations have invested and devoted resources to utilize and incorporate blockchain into their businesses. The use of distributed ledgers or blockchain has been explored in areas such as compliance and securities settlement, and these technologies could also be used to improve efficiencies in asset management.

In this chapter, we provide a short discussion of AI and blockchain applications in asset management and understand the benefits and the shift in processes, as well as the challenges that need to be overcome for practical applications for AI and blockchain and how to approach such innovations.

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

Access this chapter

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

Notes

  1. 1.

    “Britain Urged to Take Ethical Advantage in Artificial Intelligence,” John Thornhill, Financial Times. 16 April 2018. Available at: https://www.ft.com/content/b21d1fb8-3f3e-11e8-b9f9-de94fa33a81e

  2. 2.

    FinTech—How Exponential Technological Progress will affect Asset & Wealth Management https://finlantern.com/fundforum/wp-content/uploads/2017/12/FACTSET_FinTech-how-exponential-technological-progress-will-affect-Wealth-Mana…pdf

  3. 3.

    https://emerj.com/ai-sector-overviews/machine-learning-in-investment-management-and-asset-management/

  4. 4.

    “Artificial intelligence: The Next Frontier for Investment Management Firms,” Deloitte, 2019.

  5. 5.

    “Megabanks in Japan Embrace Artificial Intelligence”, Robot Technology. 30 October 2017. Available at: https://business.inquirer.net/239571/megabanks-japan-embrace-artificial-intelligence-robot-technology

  6. 6.

    http://mebfaber.com/2011/08/12/where-the-black-swans-hide-andthe-ten-best-days-myth

  7. 7.

    https://www.bcg.com/en-sea/publications/2016/blockchain-thinking-outside-the-blocks.aspx

  8. 8.

    https://sokodirectory.com/2018/01/blockchain-and-its-impact-to-the-investment-industry/

  9. 9.

    AML refers to anti-money laundering, CTF is counter-terrorism funding and KYC is know-your-client.

References

  • Alarie, B., Niblett, A., & Yoon, A. H. (2016). Using Machine Learning to Predict Outcomes in Tax Law. Canadian Business Law Journal, 58, 231.

    Google Scholar 

  • Aldridge, I., & Krawciw, S. (2017). Real-Time Risk: What Investors Should Know About FinTech, in High-Frequency Trading, and Flash Crashes. Hoboken, NJ: John Wiley & Sons, Inc.

    Book  Google Scholar 

  • Athey, S., & Imbens, G. W. (2017). The State of Applied Econometrics: Causality and Policy Evaluation. Journal of Economic Perspectives, 31(2), 3–32.

    Article  Google Scholar 

  • Bank for International Settlements. (1997, March). Real-Time Gross Settlement Systems. Basel.

    Google Scholar 

  • Bauguess, S. W. (2017). The Role of Big Data, Machine Learning, and AI in Assessing Risks: A Regulatory Perspective. Keynote Address: OpRisk North America.

    Google Scholar 

  • BCG. (2016). A Strategic Perspective on Blockchain and Digital Tokens. https://www.bcg.com/en-sea/publications/2016/blockchain-thinking-outside-the-blocks.aspx.

  • Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-Frequency Trading and Price Discovery. The Review of Financial Studies, 27(8), 2267–2306.

    Article  Google Scholar 

  • Brummer, C., & Yadav, Y. (2019). The Fintech Trilemma. Georgetown Law Journal, 107, 235–307.

    Google Scholar 

  • Buchanan, B. (2019). Artificial Intelligence in Finance. Seattle University with Funding from The Alan Turing Institute. https://doi.org/10.5281/zenodo.2612537

  • Chakravorty, G. (2016). What Is the Difference Between AI and AlgoTrading? Retrieved from https://www.quora.com/What-is-the-difference-between-AI-trading-and-algo-trading

  • Citi. (2018, March). Bank of the Future: The ABCs of Digital Disruption in Finance. CitiReport.

    Google Scholar 

  • Das, S. R. (2017). The Future of FinTech. Retrieved from https://srdas.github.io/Papers/fintech.pdf

  • Ernst and Young. (2017). Blockchain Innovation in Wealth and Asset Management: Benefits and Key Challenges to Adopting This Technology.

    Google Scholar 

  • FSB. (2017). Artificial intelligence and machine learning in financial services: Market developments and financial stability implications. https://www.fsb.org/wpcontent/uploads/P011117.pdf

  • Future Today Institute. (2017). Tech Trends Annual Report. Retrieved from https://futuretodayinstitute.com/2017-tech-trends/

  • Hasbrouck, J., & Saar, G. (2013). Low Latency Trading. Journal of Financial Markets, 16, 646–679.

    Article  Google Scholar 

  • Hendershott, T., & Riordan, R. (2013). Algorithmic Trading and the Market for Liquidity. Journal of Financial and Quantitative Analysis, 48(4), 1001–1024.

    Article  Google Scholar 

  • Kaplan, J. (2016). Artificial Intelligence: What Everyone Needs to Know. Oxford University Press.

    Google Scholar 

  • Kirilenko, A. A., & Lo, A. W. (2013). Moore’s Law Versus Murphy’s Law: Algorithmic Trading and Its Discontents. Journal of Economic Perspectives, 27(2), 51–72.

    Article  Google Scholar 

  • Martinez, V. H., & Rosu, I. (2013). High Frequency Traders, News and Volatility. In AFA 2013 San Diego Meetings Paper.

    Google Scholar 

  • McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. (1955). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.

    Google Scholar 

  • Menkveld, A. J. (2016). The Economics of High-Frequency Trading: Taking Stock. Annual Review of Financial Economics, 8, 1–24.

    Article  Google Scholar 

  • Mohamed, H., & Ali, H. (2019). Blockchain, Fintech and Islamic Finance—Building the Future of the New Islamic Digital Economy. Boston/Berlin: De|G Press.

    Google Scholar 

  • Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87–106.

    Google Scholar 

  • Peers, R. (2018). Digital Super Powers – The Role of Artificial Intelligence in Wealth Management. In S. Chishti & T. Puschmann (Eds.), The WEALTHTECH Book: The Fintech Handbook for Investors, Entrepreneurs and Finance Visionaries. Hoboken: Wiley.

    Google Scholar 

  • Rohner, P., & Uhl, M. (2017). Robo-Advisors vs. Traditional Investment Advisors – An Unequal Game. Journal of Wealth Management, 21(1), 44–50.

    Google Scholar 

  • Wuermaling, J. (2018). Artificial Intelligence in Finance: Six Warnings from a Central Banker. Intervention at the 2nd Annual Fintech Conference, Brussels.

    Google Scholar 

  • Wuzhen Institute. (2017, August). Global AI Development Report. Retrieved from http://sike.news.cn/hot/pdf/25.pdf, www.iwuzhen.org

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hazik Mohamed .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s)

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mohamed, H. (2021). I-FinTech and Its Value Proposition for Islamic Asset and Wealth Management. In: Billah, M.M. (eds) Islamic FinTech. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-45827-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-45827-0_14

  • Published:

  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-030-45826-3

  • Online ISBN: 978-3-030-45827-0

  • eBook Packages: Economics and FinanceEconomics and Finance (R0)

Publish with us

Policies and ethics