A Comparative Analysis of Machine Learning Approaches in Sukuk Price Estimation Across Global Regions

Aug 2, 2026

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Sukuk, or Islamic bonds, are a major component of Islamic finance, providing Shariah-compliant investment opportunities with increasing relevance in global markets. This study reviews how machine learning neural network methods can improve Sukuk price estimation across regions with strong Sukuk investment interest, gauged through Muslim population size. A systematic literature search across academic databases selects recent five-year publications discussing both Sukuk and machine learning, emphasizing outcome-focused evidence on precision and effectiveness. Results indicate artificial neural networks outperform traditional statistical approaches, while limitations such as short dataset sizes and simplified rating categories suggest opportunities for further algorithmic and real-time model refinement.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Introduction

Motivation

Objective

A Comparative Analysis of Machine Learning Approaches in Sukuk Price Estimation Across Global Regions Gazi Taufiq Islam1, Surajit Malakar2, Khondekar Lutful Hassan3, Rajesh Dey4 ,Rupali A Mahajan5 and Salina Kassim6 1Department of Computer Science & Engineering, Aliah University, Kolkata, India [gazi.taufiq.islam@gmail.com](gazi.taufiq.islam@gmail.com) 2Department of Information Technology, Gopal Narayan Singh University, Sasaram, India [msurajit@rediffmail.com](msurajit@rediffmail.com) 3Department of Computer Science & Engineering, Aliah University, Kolkata, India [klh.cse@gmail.com](klh.cse@gmail.com) 4IIUM Institute of Islamic Banking and Finance, International Islamic University Malaysia, Kuala Lumpur, Malaysia [rajesh.dey@gnsu.ac.in](rajesh.dey@gnsu.ac.in) 5Rupali Atul Mahajan,Associate Professor & Head, CSE(Data Science Department) Associate Dean (Research and Development) Vishwakarma Institute of Information Technology,Survey No. 2/3/4 Kondhwa (Budruk), Pune 411 048, INDIA email [id : ](id : rupali.mahajan@viit.ac.in)[rupali.mahajan@viit.ac.in](id : rupali.mahajan@viit.ac.in) 6IIUM Institute of Islamic Banking and Finance, International Islamic University Malaysia, Kuala Lumpur, Malaysia [ksalina@iium.edu.my](ksalina@iium.edu.my) ABSTRACT Sukuk, also known as Islamic bonds, constitute a significant aspect of Islamic finance, offering Shariah-compliant investment opportunities. Motivated by the increasing prominence of Sukuk in global financial markets and their potential for economic development, this study aims to investigate the effectiveness of machine learning neural networks in Sukuk price estimation. The objective is to evaluate the accuracy and efficiency of various machine learning techniques across diverse global regions with significant interest in Sukuk investment, as determined by the size of the Muslim population. The methodology for literature selection involves a systematic search of academic databases and scholarly repositories, focusing on recent publications within the last five years. Search terms include keywords related to Sukuk and machine learning. Selected papers are screened based on titles and abstracts to ensure relevance to the research topic, prioritizing those that explicitly discuss both Sukuk and machine learning. In addition, articles are evaluated for outcome-based research, particularly those that offer conclusions about the precision and effectiveness of Sukuk pricing or machine learning-based forecasting. The findings suggest that artificial neural networks perform better than traditional statistical methods in Sukuk price estimation. However, restrictions including short dataset sizes, the omission of Sukuk backed by assets, and overly basic rating categories indicate areas that warrant additional investigation. Future studies could explore comparative analyses of different machine learning algorithms, refine models for dynamic market conditions, and incorporate real-time data integration to enhance Sukuk price forecasting accuracy. Considering these drawbacks, the results highlight how machine learning might enhance the effectiveness and precision of Sukuk pricing. Key Words: Sukuk, Machine Learning, Predictions, Islamic Finance 1 Copyrights @ ICWMR-2024 Electronic copy available at: [https://ssrn.com/abstract=4945306](https://ssrn.com/abstract=4945306) 1. INTRODUCTION Islamic bonds, or sukuk, are a unique type of fixed-income investment that adheres to Islamic investment principles by granting investors beneficial ownership of certain assets [1] . The sukuk market has experienced significant expansion, attracting scholarly interest in comprehending its essential features, financial theories, and performance in stock markets [2] . Sukuk issuances have expanded beyond conventional Islamic markets, which can be ascribed to the growing integration of Islamic banking into global financial institutions [3] . However, there is a significant

FAQs

What is the primary goal of the study on Sukuk price estimation?

To critically evaluate how machine learning techniques, especially neural networks, are applied to estimate Sukuk prices across global regions with high Sukuk investment interest.

How does the methodology select relevant literature for the review?

It uses a systematic search of academic databases for publications from the last five years, screening papers by titles and abstracts to ensure they explicitly discuss both Sukuk and machine learning.

Which modeling approach performs better according to the findings?

Artificial neural networks generally perform better than traditional statistical methods for Sukuk price estimation.

Aug 2, 2026

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