Papers by shaharima Juthi
American Journal of Scholarly Research and Innovation, 2022
Blockchain technology has emerged as a transformative force in financial systems, offering enhanc... more Blockchain technology has emerged as a transformative force in financial systems, offering enhanced security, transparency, and efficiency in banking, trade finance, and regulatory compliance. This study systematically examines the adoption trends, challenges, and impacts of blockchain integration in financial systems across developed and developing economies, utilizing a case study approach. A total of 38 case studies were reviewed, encompassing financial institutions, trade finance networks, and government-backed blockchain initiatives to provide a comparative analysis of blockchain's effectiveness in different economic contexts. The findings reveal that blockchain adoption has significantly reduced fraudulent transactions by 42%, expedited trade finance settlement times by 58%, and improved compliance efficiency by 49% in regulated financial environments. While developed economies benefit from clear regulatory frameworks and advanced digital infrastructure, developing economies leverage blockchain for financial inclusion, with mobile-based blockchain solutions increasing banking accessibility by 67% among unbanked populations in select cases. Furthermore, the study highlights blockchain's role in mitigating cyber threats, with blockchainsecured financial institutions experiencing a 47% decline in cyberattacks and improved fraud detection accuracy by 31% through AI-driven blockchain models. Additionally, blockchain integration with artificial intelligence (AI), the Internet of Things (IoT), and cloud computing has enhanced real-time financial monitoring, trade authentication, and secure data management, demonstrating its growing role in financial digitalization. Despite regulatory and infrastructure challenges, blockchain presents a robust framework for fostering secure, efficient, and inclusive financial transactions globally. This study provides valuable insights for financial institutions, policymakers, and technology developers seeking to maximize blockchain's potential in reshaping global finance and trade ecosystems.
American Journal of Advanced Technology and Engineering Solutions, 2025
The integration of Artificial Intelligence (AI) into Customer Relationship Management (CRM) has r... more The integration of Artificial Intelligence (AI) into Customer Relationship Management (CRM) has revolutionized the financial services industry by enhancing customer engagement, fraud detection, predictive analytics, regulatory compliance, and marketing strategies. This study systematically reviews 83 scholarly studies, including peer-reviewed journal articles, industry reports, and financial institution case studies, to assess AI’s impact on financial CRM. The findings indicate that AI-powered chatbots, virtual assistants, and sentiment analysis tools have significantly improved customer interactions, reducing response times by 57% and operational costs by 38%, while increasing customer retention rates by 28%. AI-driven fraud detection systems have enhanced transaction monitoring, reducing false positives by 52% and improving fraud detection efficiency by 74%, leading to a 43% decrease in financial losses related to fraud. Predictive analytics has transformed credit risk assessment, improving loan approval accuracy by 67%, expediting loan processing by 29%, and reducing default rates by 23%. AI has also optimized regulatory compliance by automating Know Your Customer (KYC) and Anti-Money Laundering (AML) processes, increasing compliance accuracy by 58% and reducing penalties by 37%. Additionally, AI-driven marketing strategies have strengthened customer targeting, increasing engagement by 53% and boosting product adoption rates by 31%, while Customer Lifetime Value (CLV) models have contributed to a 27% increase in long-term customer retention and a 22% improvement in per-customer profitability. This study provides a comprehensive analysis of AI-driven CRM’s measurable benefits in financial services, demonstrating its role in enhancing decision-making, streamlining operations, improving financial security, and fostering long-term customer loyalty. The findings contribute to the expanding literature on AI in financial CRM and offer strategic insights for financial institutions, policymakers, and technology developers aiming to optimize AI adoption for sustainable growth and competitive advantage.
Innovatech Engineering Journal, 2025
The integration of Artificial Intelligence (AI), Machine Learning (ML), and
Deep Learning (DL) h... more The integration of Artificial Intelligence (AI), Machine Learning (ML), and
Deep Learning (DL) has revolutionized business strategies, enabling
organizations to enhance decision-making, optimize operations, and achieve
competitive advantages. This systematic review examines the transformative role
of these technologies in reshaping business strategies across various industries.
A total of 115 peer-reviewed articles were systematically analyzed following the
PRISMA guidelines to ensure transparency, rigor, and reliability. The study
identifies key applications of AI, ML, and DL in marketing, supply chain
management, financial analytics, and human resource management,
showcasing their ability to address complex business challenges. Additionally,
emerging trends such as Explainable AI, AI integration with IoT and blockchain,
and AI-powered sustainability initiatives are discussed, highlighting their
potential to redefine traditional business practices. Despite these advancements,
challenges such as algorithmic bias, data quality issues, implementation costs,
and the lack of regulatory frameworks remain significant barriers to adoption.
The review also identifies critical research gaps, including limited studies on AI
adoption in small and medium-sized enterprises (SMEs) and developing
economies. By synthesizing insights from these articles, this study provides a
comprehensive understanding of how AI, ML, and DL are shaping modern
business strategies, offering valuable directions for future research and practical
implementation.
Journal of Next-Gen Engineering Systems, 2025
Big Data Analytics (BDA) has emerged as a transformative force in
healthcare, offering innovativ... more Big Data Analytics (BDA) has emerged as a transformative force in
healthcare, offering innovative solutions to analyze large and complex
datasets for actionable insights. This systematic review, encompassing 142
peer-reviewed studies published between 2010 and 2024, explores the tools,
techniques, and applications of BDA in healthcare. The findings reveal the
critical role of BDA in enhancing clinical decision-making, optimizing
hospital workflows, and advancing medical research. Key applications such
as predictive analytics for disease prevention, real-time monitoring through
IoT integration, and precision medicine through genomic analysis are
highlighted. Tools like Hadoop, Spark, and TensorFlow, combined with
advanced techniques such as machine learning and natural language
processing, have been pivotal in transforming healthcare data into
actionable knowledge. However, the review also identifies significant
challenges, including data integration issues, algorithmic bias, and ethical
concerns related to patient privacy and data security. By addressing these
barriers, BDA has the potential to revolutionize healthcare delivery,
providing more personalized, efficient, and equitable care. This study
provides a comprehensive understanding of the current state of BDA in
healthcare, its limitations, and its promising future applications, offering
valuable insights for researchers, policymakers, and healthcare
practitioners.
Journal of Machine Learning, Data Engineering and Data Science, 2024
• This systematic review examines the transformative role of AI-driven models in credit scoring, ... more • This systematic review examines the transformative role of AI-driven models in credit scoring, highlighting their advances over traditional statistical methods in terms of predictive accuracy, adaptability, and inclusivity. By synthesizing findings from 70 studies, this review demonstrates that machine learning techniques, particularly ensemble models such as random forests and gradient boosting, effectively capture complex, non-linear relationships in borrower data, providing more accurate risk assessments across diverse demographics. Deep learning models, especially convolutional and recurrent neural networks, extend credit scoring capabilities to unstructured and alternative data sources, supporting financial inclusion by enabling assessments of individuals without traditional credit histories. Hybrid models that integrate logistic regression with neural networks offer an optimal balance between interpretability and predictive power, addressing regulatory demands for transparency while maintaining robust accuracy. Ensemble techniques like stacking and blending enhance model adaptability, allowing credit scoring systems to integrate multiple perspectives and improve prediction accuracy in varied borrower contexts. Despite these advancements, challenges remain in the form of ethical concerns and the need for model interpretability, particularly with complex deep learning architectures. The review underscores the importance of developing fairness-aware and explainable AI frameworks to ensure that as AI-driven credit scoring evolves, it remains both transparent and equitable. These insights suggest that with careful attention to ethics and transparency, AI has the potential to create a more inclusive and resilient credit scoring landscape, accommodating the needs of an increasingly diverse global population.
ACADEMIC JOURNAL ON BUSINESS ADMINISTRATION, INNOVATION & SUSTAINABILITY , 2024
This systematic review investigates the role of Environmental, Social, and Governance (ESG) data ... more This systematic review investigates the role of Environmental, Social, and Governance (ESG) data in corporate financial performance and investment decision-making. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 1,200 articles were initially identified, and through a rigorous screening process, 70 peer-reviewed studies were included in the final analysis. The review reveals significant findings on the positive correlation between high ESG scores and improved long-term financial returns, particularly during periods of market volatility such as the COVID-19 pandemic. Companies with strong ESG practices, especially in areas of environmental sustainability and corporate governance, demonstrated enhanced risk management capabilities and financial resilience. The study also highlights notable sectoral and geographical differences, where environmental factors had a more pronounced impact in high-risk industries like energy and manufacturing, and companies in regions with stricter ESG regulations, such as Europe, showed better financial outcomes. Furthermore, the growing role of AI and machine learning in ESG data collection and analysis is emphasized, as these technologies enable more accurate, real-time risk assessment and decision-making. However, challenges remain in the standardization of social and governance metrics, and there is a need for more longitudinal studies to fully understand the long-term impact of ESG practices. Overall, this review provides a comprehensive analysis of the current state of ESG research and its implications for sustainable corporate performance and investment strategies.
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Papers by shaharima Juthi
Deep Learning (DL) has revolutionized business strategies, enabling
organizations to enhance decision-making, optimize operations, and achieve
competitive advantages. This systematic review examines the transformative role
of these technologies in reshaping business strategies across various industries.
A total of 115 peer-reviewed articles were systematically analyzed following the
PRISMA guidelines to ensure transparency, rigor, and reliability. The study
identifies key applications of AI, ML, and DL in marketing, supply chain
management, financial analytics, and human resource management,
showcasing their ability to address complex business challenges. Additionally,
emerging trends such as Explainable AI, AI integration with IoT and blockchain,
and AI-powered sustainability initiatives are discussed, highlighting their
potential to redefine traditional business practices. Despite these advancements,
challenges such as algorithmic bias, data quality issues, implementation costs,
and the lack of regulatory frameworks remain significant barriers to adoption.
The review also identifies critical research gaps, including limited studies on AI
adoption in small and medium-sized enterprises (SMEs) and developing
economies. By synthesizing insights from these articles, this study provides a
comprehensive understanding of how AI, ML, and DL are shaping modern
business strategies, offering valuable directions for future research and practical
implementation.
healthcare, offering innovative solutions to analyze large and complex
datasets for actionable insights. This systematic review, encompassing 142
peer-reviewed studies published between 2010 and 2024, explores the tools,
techniques, and applications of BDA in healthcare. The findings reveal the
critical role of BDA in enhancing clinical decision-making, optimizing
hospital workflows, and advancing medical research. Key applications such
as predictive analytics for disease prevention, real-time monitoring through
IoT integration, and precision medicine through genomic analysis are
highlighted. Tools like Hadoop, Spark, and TensorFlow, combined with
advanced techniques such as machine learning and natural language
processing, have been pivotal in transforming healthcare data into
actionable knowledge. However, the review also identifies significant
challenges, including data integration issues, algorithmic bias, and ethical
concerns related to patient privacy and data security. By addressing these
barriers, BDA has the potential to revolutionize healthcare delivery,
providing more personalized, efficient, and equitable care. This study
provides a comprehensive understanding of the current state of BDA in
healthcare, its limitations, and its promising future applications, offering
valuable insights for researchers, policymakers, and healthcare
practitioners.
Deep Learning (DL) has revolutionized business strategies, enabling
organizations to enhance decision-making, optimize operations, and achieve
competitive advantages. This systematic review examines the transformative role
of these technologies in reshaping business strategies across various industries.
A total of 115 peer-reviewed articles were systematically analyzed following the
PRISMA guidelines to ensure transparency, rigor, and reliability. The study
identifies key applications of AI, ML, and DL in marketing, supply chain
management, financial analytics, and human resource management,
showcasing their ability to address complex business challenges. Additionally,
emerging trends such as Explainable AI, AI integration with IoT and blockchain,
and AI-powered sustainability initiatives are discussed, highlighting their
potential to redefine traditional business practices. Despite these advancements,
challenges such as algorithmic bias, data quality issues, implementation costs,
and the lack of regulatory frameworks remain significant barriers to adoption.
The review also identifies critical research gaps, including limited studies on AI
adoption in small and medium-sized enterprises (SMEs) and developing
economies. By synthesizing insights from these articles, this study provides a
comprehensive understanding of how AI, ML, and DL are shaping modern
business strategies, offering valuable directions for future research and practical
implementation.
healthcare, offering innovative solutions to analyze large and complex
datasets for actionable insights. This systematic review, encompassing 142
peer-reviewed studies published between 2010 and 2024, explores the tools,
techniques, and applications of BDA in healthcare. The findings reveal the
critical role of BDA in enhancing clinical decision-making, optimizing
hospital workflows, and advancing medical research. Key applications such
as predictive analytics for disease prevention, real-time monitoring through
IoT integration, and precision medicine through genomic analysis are
highlighted. Tools like Hadoop, Spark, and TensorFlow, combined with
advanced techniques such as machine learning and natural language
processing, have been pivotal in transforming healthcare data into
actionable knowledge. However, the review also identifies significant
challenges, including data integration issues, algorithmic bias, and ethical
concerns related to patient privacy and data security. By addressing these
barriers, BDA has the potential to revolutionize healthcare delivery,
providing more personalized, efficient, and equitable care. This study
provides a comprehensive understanding of the current state of BDA in
healthcare, its limitations, and its promising future applications, offering
valuable insights for researchers, policymakers, and healthcare
practitioners.