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Leveraging Big Data and AI for Optimizing Health Insurance Claims and Risk Assessment in Healthcare Financing
This research elucidates the transformative potential of big data analytics and artificial intelligence in optimizing health insurance claims and risk assessment by employing an empirically robust framework encompassing reliability and validity metrics, Heterotrait-Monotrait Ratio (HTMT) analysis, and bootstrapping to unravel the intricate interdependencies among constructs such as AI model accuracy, claims processing efficiency, cost efficiency, data quality, fraud detection accuracy, system usability, and user trust interface, thereby advancing a comprehensive understanding of the systemic synergies that enhance predictive precision, operational scalability, and equitable resource allocation within the healthcare financing paradigm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Leveraging Blockchain Technology forInternet ofThings Powered Banking Sector
Banking sector contributes to 70% of Indian Gross Domestic Product (GDP) and for India to meet its economic aspirations, it should enable this vivacious sector to grow at 810 times of its current pace, in the next ten years. This pace of active growth requires a double engine of sophisticated technology and a tech enabled, scalable, and a secured banking system. Implementing BlockchainTechnology (BCT) in the banking sector, provides a realistic solution which when coupled with devices connected by the Internet of Things(IoT), will result in secured, fast-paced, cost effective, and transparent growth of the sector. The prevalence of personalized banking, secured banking, connected banking, and digital banking are use cases, made possible through interface with IoT. This chapter delves into the opportunities in the banking sector to be explored and challenges to be met in the BCT-IoT implementation process. BCT- and IoT-based opportunities such as peer-to-peer lending, Know Your Customer (KYC) updation, Cross-border transfer payments, syndicate lending, fraud reduction are some of the banking operations that are elaborated. To strengthen the banking network, the consensus algorithm of Blockchainnetwork is much required and the use of IoT devices to act as nodes is pertinent. The blend of both in the banking space has to be further reinforced. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Leveraging Business Intelligence to Explore AI-Driven Transformation in the VUCA and BANI World
Artificial Intelligence (AI)-driven technological advancements often create competitive business models especially in the unpredictable BANI and VUCA environments. This chapter focuses on the role AI in transforming Business Intelligence (BI) while allowing efficient decision-making and strategic planning. The AI can transform a business using predictive analytics for volatility, automation to address complexity, decision support in non-linear scenarios, personalized user experiences, reliance on adaptive systems, improving employee productivity, and promoting sustainability. A detailed plan is implemented including resource allocation and timeline for the tasks. Using a mixed class agile methodology, a case study is developed for managing the pandemic crisis in the future for the BANI world. The BI-driven insights regarding mortality rates, infection spread, and economic impact help policymakers form informed and effective decisions. The results demonstrate that AI-driven BI offers improved efficiency, accuracy, scalability, and cost reduction in the BANI world. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Leveraging Circular Economy Principles and Emerging Technologies for Future Trends in Sustainable Business Models: A Roadmap to Net-Zero Emissions
This study examines the influence of circular economy concepts and emerging technology on the development of sustainable business models aimed at achieving net- zero emissions. It underscores the significance of resource efficiency, waste reduction, and lifecycle consideration in promoting sustainability. We analyse critical technologies such as blockchain, artificial intelligence, and the Internet of Things for their potential to enhance transparency, simplify processes, and foster stakeholder collaboration. We suggest a detailed roadmap that guides organisations in incorporating these ideas and technologies to establish robust, environmentally sustainable business practices for a sustainable future. 2026 by IGI Global Scientific Publishing. -
Leveraging cloud technology for sustainable innovation ecosystem: An ethico-legal perspective
Technological advancements affect the environment at great cost. The shift to cloud computing has intensified ethical and legal tensions over privacy, jurisdiction and regulatory requirements. The attention towards the socio-environmental consequences of economic and industrial interference is growing as corporations stress on the sustainable aspects of supply chain and product creation. The transformation of cloud technology to a sustainable innovation ecosystem depends on several ethical practices. The objective of this study is to analyse the critical factors required for the adoption of ethical, legal, and regulatory compliance for cloud technology in a sustainable innovation ecosystem. Design/Methodology/Approach: Under the doctrinal approach, the authors used an analytical method to analyse the ethical and legal perspective for the use of cloud technology in the innovation ecosystem. The authors critically analyse the international, regional and national regulatory guidelines concerning cloud technology. Findings: The study shows the lack of universally accepted framework to regulate the usage of cloud technology in a sustainable innovation ecosystem. Keeping in mind the ethical and legal impact of technology on the society and environment, the study insists global attention. Limitations: The analysis of the ethical and legal implications on the managerial and administrative aspects supports the stakeholders to comprehend the issues (ethical, legal and social) in relation to cloud technology. The study limits to focus on ethical and legal implications concerning usage of cloud technology in innovation ecosystem. Research Originality: This study critically explores the factors responsible for the rising ethical and legal issues in the usage of cloud technology in the tech-driven world. 2025 Ananya Pandey, Jipson Joseph, Amit Joshi and Vikas Kumar. All rights reserved. -
Leveraging Cybersecurity in the Context of Digital Arrest, Fraud, and Identity Theft: An Ethico-Legal Perspective
The cyber world is becoming increasingly complex due to the diverse nature of cybercrimes. Although crimes like fraud, harassment, and identity theft exist in both the cyber and physical worlds, their magnitude is unpredictably high in the cyber world. However, digital arrest fraud is unique to cyberspace. Digital arrest fraud and identity theft are the top-listed cybercrimes, causing unimaginable monetary loss, anxiety, depression, fear, and isolation among the victims. The current cybersecurity measures and tools, as well as the legal and ethical framework, fail to address the evolving nature of cybercrimes. Such a scenario demands more collaborative efforts and a renewed approach to ensure global cybersecurity. From this perspective, this chapter critically examines the evolving nature of cybercrimes and the available legal and ethical framework to underline the importance of leveraging cybersecurity through some required changes in the ethico-legal landscape. It argues for a globally accepted regulatory system and international cooperation to ensure cybersecurity. Copyright 2026, IGI Global Scientific Publishing. -
Leveraging data sharing for enhanced experiences in service industries: Role of experience orientation and privacy calculus
In the context of customer experience, the acquisition of personal and behavioural data plays an integral role, yet it raises significant privacy concerns. This study suggests employing the privacy calculus framework to craft a meaningful customer experience while addressing these privacy apprehensions. Drawing from an extensive review of literature, the study formulates propositions, subsequently validated through case studies. Beyond existing literature, this research introduces novel factors pertinent to the service industry, expanding the understanding of privacy-tradeoff dynamics. Factors such as brand reputation, transaction frequency, campaign design, grievance redressal mechanisms, ethical conduct, and marketer behaviour emerge as pivotal triggers influencing privacy-tradeoff decisions. However, empirical validation is required to consolidate these propositions. The study highlights the imperative of bridging the privacy calculus concept with customer experience, particularly within the service sector, shedding light on factors that can effectively navigate this terrain. By clarifying the relationships between privacy considerations and customer experience, this chapter contributes to a deeper comprehension of the intricacies involved. It not only identifies key factors but also underlines their potential to shape and optimize the customer journey amidst privacy concerns. The proposed framework offers a better understanding of how businesses can navigate the delicate balance between data privacy and delivering exceptional service. The study recommends further empirical scrutiny to validate and refine the proposed framework, ultimately fostering a more robust understanding of privacy dynamics in the service industry. 2025 Elangovan N., Aishwarya Nagarathinam, Sundaravel Elangovan, Aarthy Chellasamy and Sangeetha Rangasamy. Published under exclusive licence by Emerald Publishing Limited. All rights reserved. -
Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised Cloud Computing Approach
Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. 2024 IEEE. -
Leveraging Deep Learning for Early Detection of Alzheimer's Disease from MRI Scans
Alzheimer's disease (AD) remains shrouded in mystery, with its early detection posing a significant challenge. This research paper delves into the cutting-edge realm of deep learning, exploring its potential to explore the brain's secrets and revolutionize AD diagnostics using Magnetic Resonance Imaging (MRI) data. Upon comprehensively reviewing the performance of six state-of-the-art models and studying their strengths and limitations on MRI data, this paper proposes a novel deep-learning architecture based on the InceptionV3 model for Alzheimer's Disease prediction using MRI data. The proposed architecture leverages convolutional neural networks (CNNs) to extract subtle brain structure and function patterns, potentially identifying early AD signatures before noticeable cognitive decline. The proposed model is validated on a large-scale MRI dataset that comprises four stages of dementia, demonstrating more insights. Inception V3 base model yielded 82% accuracy, measured using the metric Area Under the Curve (AUC), on the dataset, and an improved AUC of 87% was achieved by performing data augmentation to remove the class imbalance in the dataset. The proposed deep learning model built on top of Inception V3 exhibited an improved performance with an AUC of 88% underlining the potential of deep learning models in early AD detection. The paper's findings will contribute to the ongoing effort to revolutionize AD diagnosis and accelerate the development of personalized treatment strategies. Grenze Scientific Society, 2025. -
Leveraging Deep Learning for Early Detection of Autism Spectrum Disorder in Augmented and Virtual Reality Mental Healthcare Environments
The advent of digital health interventions offers new vistas for the early detection and management of autism spectrum disorder (ASD). This paper explores the innovative application of deep learning algorithms within augmented reality (AR) and virtual reality (VR) environments to enhance the early detection of ASD. Integrating AR and VR in mental healthcare provides immersive, controlled settings in which individuals behaviors and responses can be observed and analyzed in real time. We propose a novel framework that utilizes deep learning techniques to process and analyze the data collected from these digital environments. Our approach leverages facial expression analysis, eye tracking, and behavioral pattern recognition to identify early signs of ASD. By capturing subtle nuances in behavior often overlooked in traditional diagnostic settings, our method offers a non-invasive, engaging, and efficient alternative for early ASD detection. The potential of this technology extends beyond diagnosis, offering possibilities for personalized intervention strategies that can be adapted to each individual's unique needs. Our findings indicate that deep learning in AR and VR environments could significantly enhance the accuracy and timeliness of ASD diagnosis, paving the way for earlier and more effective interventions. This study underscores the importance of interdisciplinary collaboration in developing innovative healthcare solutions, combining the fields of artificial intelligence, psychology, and digital media to improve outcomes for individuals with ASD. 2026 Scrivener Publishing LLC. -
Leveraging Deep Learning in Hate Speech Analysis on Social Platform
The scope and usage of the Internet have surpassed the expected growth and have proven beyond the basic purpose of being used for networking and telecommunications. It serves as the backbone of the web, and one of the predominant domains that uses the Internet is social media. The concept was conceived in the early 1990s and went on to grow as a powerful medium of people networking along with the Internet. Social networking sites (SNS) acquired a predominant element of the Internet owing to their use and services they offer through the Internet. A few of the most used social networking sites include Twitter and Facebook, which are used synonymous to expressions of text. These SNS allow the users to post photos, videos, and other multimedia content along with text and voice messages that are shared among other users. As with any technology or application, these also have the risk of users posting offensive material and textual content. Hate is being spread through messages, which are in the form of text and also through other materials posted. There is no control to check for the message for the hate content as and when it is posted, and by the time it is deleted by admins, it could have already reached millions of users. This chapter proposes a technique for detecting hate texts in reviews from registered users in the Twitter dataset. The proposed work makes use of improved principle component analysis (IPCA) and modified convolution neural network (MCNN) for detecting hate texts. The advantage of natural language processing is used for building an automated system for the analysis of syntax and semantics of the words. The proposed methodology consists of phases like pre-processing, feature extraction, and process to classify the text. The white spaces in the text are removed through normalization in the pre-processing phase, and also remove special characters such as question marks, punctuations, and exclamatory symbols to remove stop words. The features that are pre-processed are then subjected to feature extraction using IPCA. A set of correlated features are made used for identifying more important features in the data set under consideration. Next, the classification is done for identifying the hate text or for any language abuse. MCNN is applied for the classification of the text into HATE and NON-HATE from the text with better accuracy. The experiments prove that the proposed method has a high level of accuracy even for a large dataset. The results show that the proposed method has better performance in terms of precision, recall, and F-measure when compared with other state-of-the-art methods. 2024 Taylor & Francis Group, LLC. -
Leveraging digital yarn dyeing for colour consistency in apparel weaving
When compared to traditional processes, digital yarn dyeing provides substantial benefits in terms of color control, versatility, and environmental impact. However, technological obstacles and constraints exist. The promise of digital dyeing may be realized by carefully selecting technology, optimizing ink consumption, and adopting stringent quality control methods, resulting in improved colour constancy and a more sustainable textile sector. -
Leveraging Employee Data to Optimize Overall Performance: Using Workforce Analytics
Consistent employee performance is necessary for timely achievement and business success. Many key performance indicators influence an employees organizational performance, such as employee satisfaction, employee work environment, relationship with managers and coworkers, work-life balance, and many more. It becomes critical to regularly understand how these factors are connected to employee performance. One such method that is commonly used in companies is workforce analytics. It is a process that uses data-based intelligence for improving and enhancing management decisions in hiring and constructing compensations in alignment with employee performance. This also helps the management make data-based decisions and predictions, which helps in cost reductions and increases the overall profit. This chapter aims to analyze and report the workforce-related data and visualize the performance of 1,470 employees using published IBM human resources (HR) data made available at https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781003357070/bb25a486-c036-4524-ab00-446f8eda3fd1/content/www.Kaggle.com xmlns:xlink=https://www.w3.org/1999/xlink>Kaggle.com. The chapter considers the following factors - job involvement, job satisfaction, performance rating, relationship satisfaction, environmental satisfaction, employee tenure, work-life balance, and income level - for data analysis and visualization of employee performance. The chapter aims to adopt descriptive, diagnostic, and predictive analysis using various software like Python, the Konstanz Information Miner (KNIME), and Orange. The visualization will be made using Tableau, Power BI, and Google Data Studio. Thus, the chapter gives a comprehensive insight into the meaning and importance of workforce analytics, different technologies used in workforce analytics, workforce analytics trends and tools, challenges of workforce analytics, and the process of implementation of workforce analytics. 2024 selection and editorial matter, Alex Khang, Sita Rani, Rashmi Gujrati, Hayri Uygun, and Shashi Kant Gupta; individual chapters, the contributors. -
Leveraging ensemble learning for enhanced security in credit card transaction fraudulent within smart cities for cybersecurity challenges
In the age of digital transactions, credit cards have emerged as a prevalent form of payment in smart cities. However, the surge in online transactions has heightened the challenge of accurately discerning legitimate from fraudulent activities. This paper addresses this crucial concern by introducing a pioneering system for detecting fraudulent credit card transactions, particularly within highly imbalanced datasets, in the realm of cybersecurity. This paper proposes a hybrid model to effectively manage imbalanced data and enhance the detection of fraudulent transactions. This paper emphasizes the efficacy of the hybrid approach in proficiently identifying and mitigating fraudulent activities within highly imbalanced datasets, thereby contributing to the reduction of financial losses for both merchants and customers in smart cities. As cybersecurity in smart cities evolves, this paper underscores the significance of ensemble learning and cross-validation techniques in optimizing credit card transaction analysis and fortifying the security of digital payment systems. 2024, Taru Publications. All rights reserved. -
Leveraging Ensemble Methods for Accurate Prediction of Customer Spending Scores in Retail
This study primarily aims to estimate consumer spending trends in a retail context. The goal is to identify the best model for predicting Purchasing Scores, which indicate customer loyalty and potential income, using demographic and financial data. The dataset included information about customers' age, gender, and annual income, and the objective was to analyze their Spending Scores. Several regression models were tested, including Linear Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors (KNN), and Lasso Regression. To improve the models, we engineered features like Age Squared, Income per Age, and Spending Score per Income. Each model was trained and tested using 3fold cross-validation. We evaluated their performance with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. The results showed significant differences in model performance. The Random Forest model stood out, with the lowest Mean Absolute Error (MAE) of 0.33, Root Mean Square Error (RMSE) of 0.52, and the highest R-squared (R22) score of 0.9997. Gradient Boosting also performed well, achieving a Mean Absolute Error (MAE) of 1.77, Root Mean Square Error (RMSE) of 2.41, and an Rsquared (R2) score of 0.9930. While Linear Regression showed moderate accuracy, KNN and Lasso Regression had higher errors and lower R2 values, indicating less reliable predictions. The findings suggest that ensemble methods, particularly Random Forest, excel at predicting customer Spending Scores. The high accuracy and reliability of this model point to its potential for customer segmentation and targeted marketing strategies, ultimately enhancing customer relationship management and boosting business value. Further refinement and exploration of additional features could further improve these prediction capabilities. 2024 IEEE. -
Leveraging Financial Data to Optimize Automation: An Industry 4.0 approach
Industry 4.0 is a transformative approach that leverages advanced technologies to enhance business efficiency and productivity. Automation is a crucial aspect of next-generation industry, and leveraging financial data is essential to optimizing the automation process. This chapter discusses the role of financial data in optimizing automation processes using an I-4.0 approach. Financial data is derived from various sources and can be collected through different methods, such as automated data collection, manual entry, or using sensors and Internet of Things (IoT) devices. The integration of these sources can pose challenges for businesses. The chapter outlines techniques for automation optimization, such as machine learning, predictive analytics, and business process reengineering. Optimizing automation using financial data offers various benefits for businesses, including cost savings, improved quality, and increased profitability. However, there are challenges that businesses face in leveraging financial data, including the integration of various data sources and formats and the need for skilled personnel to analyze and interpret the data. The successful implementation of automation and optimization of processes can lead to sustainable growth and enhanced operations, making it crucial for businesses to remain competitive in the I-4.0 era. By leveraging financial data to optimize automation processes, businesses can maximize their potential and drive growth. Overall, this chapter highlights the significance of financial data in automation optimization and provides insights into the benefits and challenges that businesses must consider when leveraging financial data for optimization. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
Leveraging FinTech for the Advancement of Circular Economy
During the past six decades, there has been a lot of emphasis on increasing production and fulfilling the demands of the fast-growing population. As a result, there has been unprecedented utilization and depletion of natural resources and harm to the environment. It was rightly realized by government and policymakers that there is an indispensable need to align economic development with the environment. In other words, the world needs to pursue environmentally friendly economic development. In order to achieve sustainable development, the thought leaders devised a new approach called circular economy. The circular economy focuses on reusing and recycling materials to reduce the consumption of natural resources and minimize waste creation. In recent years, financial technology commonly known as FinTech has become a significant part of commercial activities across many industries. FinTech has benefited organizations and users in terms of cost and time saving with a high degree of reliability. This article outlines the ways in which FinTech supports the cause of a circular economy. It also explores the impediments in this path. 2024 Scrivener Publishing LLC. -
Leveraging gamification in the metaverse: Strategies for consumer engagement, innovation, and problem-solving across fashion industries
As the world adapted during the pandemic, virtual platforms became groundbreaking in terms of popularity, which brought forth the Metaverse, a transformative digital universe. This development is blurring the lines between gaming and the consumer internet and providing immersive, emotional, and socialized experiences. The variables driving deeper connections are technology readiness, user experience, and social influences. This study explores the effective use of animated agents in VR as an advertising strategy, linking findings to existing research. Much attention is given to gamification and VR, but little focus exists on how these experiences resonate with India's unique cultural context. The integration of Indian traditions into the Metaverse can revolutionize brand engagement, reshaping consumer perceptions and interactions. The paper discusses consumer engagement, readiness, and problem- solving, and it is based on the potential of culturally aligned VR experiences to transform industries, enhance connections, and create new avenues for brand immersion in the digital era. 2025, IGI Global Scientific Publishing. All rights reserved. -
Leveraging Generative AI to Address Behavioral Biases in the Investment Decisions of Gen Z and Millennials
GAI alters financial decision-making behavior through advanced data analytics, trend prediction, and personal recommendation. Generation Z and the Millennials are more susceptible to behavioral biases like herd mentality, loss aversion, overconfidence, and fear. Such tendencies make people prone to frequently exhibiting instinctive or irrational investment behaviors, thus severely impacting their long-term financial outcome. In the context of this book, the relationship between behavioral finance and GAI is discussed with the benefits of enhancing investment literacy and in guiding the younger investor towards data-driven decisions. Other areas on data quality and transparency, ethical concerns, and regulatory compliance are discussed. Hence, this can result in intelligent and rational investment decisions. The subsequent section explains how GAI successfully eliminates the effects of cognitive biases through an enhancement of the capabilities concerning decision-making in respect of financial choices related to Generation Z and millennials under the everchanging finance landscape. 2026, IGI Global Scientific Publishing. All rights reserved. -
Leveraging Green Finance for Sustainable Development: An Empirical Analysis of Economic Growth and Environmental Sustainability of Asian OECD Economies
Present study investigates the impact of Green Finance and CO2 emissions on GDP per capita of four Asian OECDeconomies controlling for Expenditure on Education, and Foreign Direct Investment using panel data for the time-period 2015 to 2023, applying pooled OLS, Fixed Effects, and Random Effects Models, and ultimately selecting the Fixed Effects Model based on robust statistical tests (Hausman and Breusch-Pagan LM), revealing that Green Finance significantly enhances GDP per capita, Expenditure on Education unexpectedly hinders it in the short term, and both CO2 emissions and Foreign Direct Investment lack statistically significant effects within countries, thereby underscoring the importance of internal structural factors and advocating for tailored, sustainability-driven, and context-sensitive economic growth strategies. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development.