Islamic Finance in Canada Powered by Big Data: A Case Study

Imran Abdool () and Mustafa Abdool ()
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Imran Abdool: Blue Krystal Technologies and Business Insights
Mustafa Abdool: Stanford University

A chapter in Big Data in Finance, 2022, pp 187-206 from Springer

Abstract: Abstract This chapter presents a case study of establishing a credit union by a Toronto Muslim community based on the principles of Islamic Finance. One of the biggest obstacles to establishing a new credit union in Ontario, Canada, is receiving regulatory approval from the provincial regulator, the Financial Services and Regulator Authority (FSRA). A core component of the application process is the collection of in-depth financial and market data from several thousand prospective members. This chapter examines the power of big data tools employed by the proposed Islamic Credit Union for Community (ICUC) to collect the massive amount of data required to receive regulatory approval. Such tools include state-of-the-art modeling techniques such as recurrent neural networks (RNNs), deep reinforcement learning, and attention mechanisms using transformers for time-series modeling. These tools are extremely useful in building dynamic and stochastic banking models along with other predictive analytics. This chapter illustrates both the methodology and practical steps for determining the feasibility of a new financial institution in a heavily regulated financial sector of a G8 country. More specifically, it shows how big data tools apply to serve the needs of a financial institution in a specialty market.

Keywords: Islamic finance; Sequential models; Recurrent neural networks; Time series modeling (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-12240-8_10

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DOI: 10.1007/978-3-031-12240-8_10

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