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Revolutionizing Islamic finance with ethical AI: Shariah-compliant Robo-advisors
The revolutionary role that robo- advisors play in Shariah- compliant Islamic finance is examined in this essay. The first section lays out the fundamental ideas of Shariah- compliant investing, which serve as a framework for moral financial judgment. Examined is the emergence of robo-a dvisors, which show how these AI-p owered tools are improving accessibility and efficiency while enabling adherence to Shariah laws. Key characteristics of robo- advisors that adhere to Shariah are examined, along with particular difficulties including cultural sensitivity and regulatory compliance. The research integrates case studies that demonstrate effective implementations, providing valuable perspectives on their real- world uses. The paper's conclusion, which offers a look ahead, highlights how ethical AI and robo- advisors have the potential to transform Islamic finance by promoting sustainability, inclusion, and creativity in Shariah- compliant investments. 2025, IGI Global Scientific Publishing. All rights reserved. -
Impact of Covid-19 on Sustainable Indices: A Structural Break Analysis
The study focused on the structural breakpoint on Covid-19 announcement by the World health organization (WHO) on March 11, 2020. The objective of the study is to know the breaks at the specified breakpoints (Covid-19 Announcement). This paper applied the CUSUM of Squares test, chow breakpoint test, and F-Statistics on daily basis the event window (?30days, Event date,?+30days) of seven emerging and sustainable indices of the world namely, viz, China, India, Brazil, Mexico, Russia, Indonesia, and Turkey. The interpretation of the graph of the series indicates that breakpoint. The findings of simple regression are significant. The stability diagnostic of CUSUM Square test degradation shown out 5% level of significance, meaning the break in the model. In the Chow test, the null hypothesis is rejected, there is a break as on covid-19 date of announcement breakpoints, the value of f-statistic is significant at 5% level. It is life-threatening to consider the change due to the Covid-19 in selected sustainable indices. Our estimation allowed counterfactual experiment to indicate that the covid-19 affected the stock market deviation on the announcement of Nobel coronavirus in the world. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Equity by Design: Embedding DEI Into AI- Enhanced Marketing Tools
In a time when artificial intelligence redefines marketing practice pillars, the concern is not innovation but conscience- driven innovation. While AI tools promise unmatched precision in reaching customers, segmenting, and budgeting, they carry a silent danger of deepening existing disparities, if they were to be unleashed without caution. This article advocates for the practice of Equity by Design, and it insists that Diversity, Equity, and Inclusion (DEI) must be made a part of the actual design of AI- driven marketing systems. By borrowing cross- disciplinary insights from finance, organizational ethics, and digital strategy, the case is argued through illustrations of how equitable design cuts down on algorithmic bias, expands financial service access to marginalized communities, and enhances consumer trust in a more data- driven market. Beyond compliance or corporate social responsibility, embedding DEI in AI is a competitive strategy, attaching ethical obligation to long- term brand worth, sustainable growth, and global competitiveness in the digital economy. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Cutting-edge technologies for economic well-being: The transformative power of AI and blockchain
This paper explores the transformative impact of cutting-edge technologies, specifically AI and blockchain, on economic well-being. It begins by examining how AI-driven solutions are revolutionizing industries, enhancing decision-making processes, and improving financial inclusion. Blockchain technology is discussed for its potential to increase transparency, reduce fraud, and streamline financial transactions, thereby fostering trust and efficiency. The synergy between AI and blockchain is highlighted as a powerful driver for economic growth, enabling decentralized finance, secure data management, and scalable innovation. The paper further explores how these technologies are unlocking new opportunities for sustainable development, particularly in emerging markets. It concludes by emphasizing the role of AI and blockchain in shaping a future of economic prosperity, with a focus on inclusivity, sustainability, and long-term well-being. 2025, IGI Global Scientific Publishing. -
The Evolving Role of AI in Cybersecurity Insurance: Enhancing Risk Assessment and Threat Detection
Cybersecurity insurance is a very important defense against data breaches, cyberattacks, and AI-powered system failures. As the use of AI becomes more widespread in business operations, insurers are now assessing emerging risks such as algorithmic errors, adversarial attacks, and data manipulation. As the cybersecurity insurance industry grows, organizations need to implement AI-specific safeguards to mitigate vulnerabilities. This study uses a mixed methods approach, by incorporating qualitative case studies for the integration of AI in claims processing with quantitative analysis on AI-driven risk assessment models. The method will be based on surveys, expert interviews, and real insurance claim analysis. Important goals involve the improvement of assessments of risk, underwriting, and claims processing through AI. Case studies of AI-enabled threat detection tools, including Darktrace, Microsoft Sentinel, and Google's phishing detection, demonstrate the AI impact in cybersecurity. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Can Greenhouse Gas Emissions Be a Driving Factor for Economic Stability? An In-depth Study of D8 Nations
This chapter examines the relationship between greenhouse gas (GHG) emissions and economic stability in D8 countries Bangladesh, Egypt, Indonesia, Iran, Malaysia, Nigeria, Pakistan, and Turkey. It aims to assess whether emissions, particularly methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2), serve as economic indicators and how transitioning to renewable energy influences economic growth and environmental sustainability. The chapter employs statistical analysis using regression models to evaluate the impact of GHG emissions on key economic indicators such as gross domestic product (GDP), gross national income (GNI), inflation, and energy consumption. Data from the World Development Indicators (WDI) spanning 20012023 are utilized. The Environmental Kuznets Curve (EKC) and Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) models are applied to examine the interplay between emissions and economic growth. Additionally, sectoral analysis is conducted to highlight the role of renewable energy and policy measures in mitigating emissions. The results indicate a complex relationship between emissions and economic stability. Methane and nitrous oxide emissions exhibit a significant correlation with economic performance, whereas CO2 emissions show mixed effects. Countries investing in renewable energy, such as Indonesia and Malaysia, demonstrate improved economic resilience while reducing emissions. The chapter highlights the need for energy efficiency measures, investment in clean energy, and regional cooperation to balance economic growth with environmental sustainability. This chapter provides a comprehensive analysis of the economic impact of GHG emissions in D8 nations, offering policy recommendations to achieve sustainable development while maintaining economic stability. 2026 Afzalur Rahman, Shakeb Akhtar, Mahfooz Alam, and Mohsin Khan and 2026 The authors. -
Green Blockchain Mechanisms: Enhancing Transparency and Efficiency in Sustainable Finance With DeFi Solutions
Blockchain is increasingly integrated into financial services, offering innovative solutions, especially in sustainability. This paper explores green blockchain as a tool for sustainable finance, focusing on its environmental applications. The research examines how green blockchain can transform financial systems through enhanced transparency, accountability, and efficiency. By recording, authenticating, and rewarding eco-friendly investments, green blockchain supports sustainable growth. The role ofDeFi in promoting transparency and optimizing financial activities is analyzed. The methodology covers industry data mechanisms. The study concludes that green blockchain can drive sustainable financial change by fostering trust and transparency, empowering investors and institutions to promote environmental responsibility within the global economy. 2025 by IGI Global Scientific Publishing. All rights reserved. -
AI's impact on financial services, auditing, and investment strategies: The future ahead
Artificial intelligence (AI) is poised to significantly transform the fields of finance, auditing, and investment over the coming decade. In financial services, AI is anticipated to enhance decision-making processes, improve risk management practices, and facilitate the delivery of personalized financial solutions. The auditing profession will be reshaped by AI and automation, enabling continuous auditing, real-time reporting, and more effective fraud detection mechanisms. In the investment domain, AI is expected to revolutionize portfolio management and asset allocation through advanced predictive analytics and data-driven market strategies. Despite these advancements, the rapid proliferation of AI presents considerable challenges, including ethical considerations, regulatory compliance, and the necessity of re-skilling financial professionals. This strategic overview examines both the opportunities and risks associated with AI integration and provides guidance for organizations seeking to maintain competitiveness in an increasingly AI-driven financial landscape. 2025, IGI Global Scientific Publishing. All rights reserved. -
Navigating Towards Sustainability: The Role of Corporate Governance and AI- Driven Behavioral Interventions
The paper explores how corporate governance practices influence non- financial performance, particularly sustainability. Strong governance ensures that ESG principles are integrated into business operations, promoting long- term sustainability. The study examines the effectiveness of AI- assisted behavioral interventions in advancing sustainable practices. AI can mitigate cognitive biases and irrational decisions, pushing individuals and organizations toward sustainability. AI- driven governance frameworks can enhance non- financial performance, including environmental stewardship, social responsibility, and ethical governance. Through case studies and analysis, the paper provides insights into how AI and corporate governance can optimize sustainability strategies beyond traditional financial metrics. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Sustainable investment portfolios with Al: Bridging green finance and technological innovation
This research examines the transformative role of artificial intelligence in advancing green finance by enhancing sustainable portfolio management practices. Through Al-driven data analysis, investors are empowered to prioritize environmentally responsible assets, such as green bonds and renewable energy funds, aligning their portfolios with climate-positive goals. Al algorithms play a critical role in forecasting environmental risks, aligning investments with ESG (Environmental, Social, and Governance) standards, and fostering ethical finance trends. This study explores a range of Al fools that enhance decision-making in green finance, assessing their strengths, limitations, and the societal benefits they can unlock. By aligning Al innovation with green finance objectives, this research advocates for a financial ecosystem that supports global sustainability and ethics. 2026 by IGI Global Scientific Publishing. All rights reserved. -
AI-Driven Human Augmentation: Enhancing Productivity, Creativity, and Decision-Making in the Business Process
This study explores how AI augments human productivity, creativity, and decisionmaking across sectors, positioning it as a tool to enhance human potential. Using a qualitative approach with case studies from healthcare, business, and creative industries-such as IBM Watson-it illustrates AI's practical applications. Central to the study are ethical concerns like data privacy, algorithmic bias, and fairness. 2026, IGI Global Scientific Publishing. All rights reserved. -
Data Ingestion - Cloud based Ingestion Analysis using NiFi
Data Ingestion has been an integral part of Data Analysis. Bringing the data from various heterogeneous sources to one common place and ensuring the data is captured in the appropriate format is the key for performing any Big data task. Data ingestion is performed using multiple frameworks across the industry and they all have their own set of benefits and drawbacks. Apache NiFi is one popular ingestion framework which is used widely and does Ingestion effectively. Ingestion is performed on various sources and the data is generally stored in clusters or cloud storage. In this paper, we have done the File Data Ingestion using the NiFi framework on a local machine and then on two cloud-based platforms, namely Google Cloud Platform (GCP) and Amazon Web Services (AWS). The objective is to understand the latency and performance of the NiFi tool on Cloud-based Ingestion and provide a comparative study against the typical Data Ingestion. The entire setup was done on a local machine and two corresponding cloud platforms namely GCP and AWS. The findings from the comparative analysis have been compiled in a tabular format and graphs are created for easy reference. The paper places emphasis on the significance of NiFi's data ingestion performance on Cloud Platform and attempts to present it as a major activity on the data ingestion platform for Cloud Ingestion Solution. 2023 IEEE. -
A Systematic Review of Challenges, Tools, and Myths of Big Data Ingestion
Each sector of the digital world generates enormous data as human life continues to transform. Areas like data analytics, data science, knowledge discovery in databases (KDD), machine learning, and artificial intelligence depend on highly distributed data which requires appropriate storage in a data lake. Collecting the data from different heterogeneous sources and creating a single lake of data is called data ingestion. Ironically, data ingestion has been treated as a less important stage in data analysis because it is considered a minor first step. There are several misconceptions in the data and analytics domain about data ingestion. The survey employed in this research presents a list of significant challenges faced by information technology (IT) industries during data ingestion. The available frameworks are compared in terms of standard parameters that are set against the existing challenges and myths. The findings from the comparison are compiled in a tabular format for easy reference. The paper places emphasis on the significance of data ingestion and attempts to present it as a major activity on the big data platform. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
File Validation intheData Ingestion Process Using Apache NiFi
In the industries of today, development and maintenance of data pipelines is of paramount importance. With large volumes of data being generated across industries on a continuous basis, there is a growing need to process and store this ingested data in a fast, and efficient manner. Apache NiFi is one such tool which possesses crucial capabilities that can be used to enhance, modify, and automate data pipelines. However, automation of the ingestion process creates certain inherent issues which, without being resolved, tend to be detrimental to the entire ingestion process. These issues vary in nature, ranging from corrupted data to changes in the file schema, to name a few. In this paper, a solution to this problem is proposed. By exploiting Apache NiFis custom processor development capabilities, problem-specific processors can be designed and deployed which can ensure accurate validation of the ingestion process on a real-time basis. To demonstrate this, two processors were developed as a proof-of-concept, which tackle specific file-related validation issues in the ingestion processthat of the file size, and, the ingestion frequency. These custom-built processors are designed to be inserted into the pipeline at key points to ensure that the ingested data is validated against certain standards and requirements. Having successfully demonstrated its capabilities, the paper presents the exploitation of Apache NiFis custom processor capabilities as a potential way forward to resolve the plethora of ingestion issues in industry, today. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
AI-driven decentralized finance and the future of finance
In the evolving landscape of finance, traditional institutions grapple with challenges ranging from outdated processes to limited accessibility, hindering the industry's ability to meet the diverse needs of a modern, digital-first society. Moreover, as the world embraces Decentralized Finance (DeFi) and Artificial Intelligence (AI) technologies, there becomes a need to bridge the gap between innovation and traditional financial systems. This disconnect not only impedes progress but also limits the potential for financial inclusion and sustainable growth. AI-Driven Decentralized Finance and the Future of Finance addresses the complexities and challenges currently facing the financial industry. By exploring the transformative potential of AI in decentralized finance, this book offers a roadmap for navigating the convergence of technology and finance. From optimizing smart contracts to enhancing security and personalizing financial experiences, the book provides practical insights and real-world examples that empower professionals to leverage AI-driven strategies effectively. With a focus on regulatory challenges, ethical considerations, and emerging trends, this book teaches individuals and organizations how to harness the power of AI in finance. By fostering interdisciplinary collaboration and offering forward-thinking perspectives, the book equips readers with the knowledge and tools needed to navigate the complexities of the digital age. Through this comprehensive exploration, AI-Driven Decentralized Finance and the Future of Finance not only offers solutions to current challenges but also paves the way for a more inclusive, sustainable, and innovative future for finance. 2024 by IGI Global. All rights reserved. -
Global Peace in Virtual World-Dispute Settlement Through on Line Resolution
SPC ERA International Journal of Business and Management, Vol-2 (3), pp. 1-3. ISSN-2347-9647 -
Non-Recombinant Mutagenesis of Bacillus mojavensis CUIE1819 for Hyper Production of Lipase and Treatment of Polluted Lakes
Microorganisms that degrade oil contribute significantly to the bioremediation of polluted lakes. Many microorganisms synthesize lipases, which are commercially significant. In the present study microorganisms producing extracellular lipase were isolated from various polluted lakes in Bangalore by using tributyrin agar. A lipase assay was done to determine the most efficient lipase-producing organism, which was then named Bacillus mojavensis CUIE1819 based on 16srRNA sequencing. After UV irradiation, the selected immobilized organisms were used to treat the lake water samples. 2022, Association of Biotechnology and Pharmacy. All rights reserved. -
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Implementing Machine Learning for Early Detection and Prognostic Modeling of Chronic Diseases
The employ of deep learning methods for the diagnosis and prognosis model of chronic diseases is an important discovery to change the healthcare service. Some of the chronic diseases which prevalence and incidence rates remain high globally include diabetes, cardiovascular diseases, chronic kidney diseases, and cancers. There is nothing more critical than early diagnosis and accurate prediction of the patients' condition and the best course of action that has to be taken. This paper aims at examining the possibility of utilizing ANN, Random Forest, XGBoost, and CNN to forecast the occurrence of the. Due to integration of big and varied data which involve clinical characteristics, biochemical parameters and medical images among others, ML models have the ability recognize complex relations not easily recognizable by conventional diagnostic procedures. These illustrations prove that deep learning models or more specifically the convolutional neural networks for image diagnosis outperform other traditional methods in performance and prognosis. Nevertheless, some issues, such as data quality, model's interpretability, and its implementation into clinical practice, are still present. The challenges appeared in this paper are key to understanding the future of ML in healthcare as they can pave the way to the integration of such models into practice, therefore leading to early detection, better prognosis, and effective management of chronic diseases. This paper aims at exploring on how ML can be of significance in transformation of the health care sector and orderly improve patients care. 2025 IEEE.