Conference Presentations by Sarvagya Jha
Legal Convention 2024, 2024
The legal profession is undergoing a paradigm shift driven by the integration of artificial inte... more The legal profession is undergoing a paradigm shift driven by the integration of artificial intelligence (AI), with Natural Language Processing (NLP) emerging as one of the most transformative technologies in contract review and due diligence. In an era where the volume
and complexity of contracts in business transactions continue to rise exponentially, traditional manual review processes are rapidly becoming inefficient, expensive, and prone to human error. As a result, law firms and legal departments are seeking innovative solutions that not only
streamline workflows but also enhance accuracy and scalability.
This research explores how NLP technology can dramatically reduce the time and resources required for contract review and due diligence while improving accuracy and consistency. We analyze the current state of NLP in legal applications, examining innovations such as machine learning algorithms for contract classification, entity extraction for key clause
identification, and semantic analysis for understanding contract language (Surden, 2019a).
However, the integration of NLP into legal practice is not without challenges. We address critical concerns including data privacy, maintenance of attorney-client privilege, and the need for human oversight in legal decision-making (Chagal-Feferkorn, 2019). The paper also discusses the implications for legal education and practice, as successful implementation of NLP systems necessitates new skills and approaches from legal professionals (McGinnis & Pearce, 2014)
Papers by Sarvagya Jha
Journal of Information Systems Engineering and Management, 2025
This research explores the integration of Artificial Intelligence (AI) and the Internet of Things... more This research explores the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in supply chain management, emphasizing their role in enabling intelligent and autonomous business operations. It examines how these technologies enhance efficiency, decision-making, and adaptability in modern supply chains. A systematic review of existing literature, case studies, and industry reports is conducted to analyze the impact of AI and IoT in supply chain processes. The research highlights that AI-driven predictive analytics and IoT-enabled real-time monitoring significantly enhance supply chain visibility, efficiency, and responsiveness. Automation through AI-powered decision-making and IoT-based smart tracking systems reduces operational risks, minimizes costs, and optimizes resource allocation.
However, challenges related to cybersecurity, data privacy, and integration complexities remain key concerns. This study contributes to both academic and industry discussions by offering insights into the evolving role of AI and IoT in supply chains. Practically, it provides guidance for
businesses seeking to implement intelligent supply chain solutions. Socially, the findings emphasize the need for ethical AI deployment, data security, and workforce upskilling to navigate the shift toward autonomous operations. This research presents a unique synthesis of AI and IoT advancements in supply chain management, highlighting their synergistic impact on creating more adaptive, resilient, and intelligent operations. By addressing both opportunities and challenges, it serves as a valuable resource for researchers, practitioners, and policymakers in the field.
Keywords: AI in supply chains, IoT in logistics, intelligent supply chains, autonomous business operations, supply chain automation, digital transformation, Industry 4.0.
Indian Journal of Integrated Research in Law, 2024
This comprehensive study examines the intricate relationship between financial derivatives, legal... more This comprehensive study examines the intricate relationship between financial derivatives, legal risk, and regulatory challenges within Over-the-Counter (OTC) markets. As OTC derivatives continue to play a pivotal role in global finance, they present unique regulatory hurdles due to their complexity, customization, and the decentralized nature of their trading. This research provides an in-depth analysis of the current regulatory framework governing OTC derivatives markets, with a particular focus on major jurisdictions including the United States and European Union. It explores key legal risks inherent in OTC derivatives transactions, such as counterparty, operational, market, and regulatory risks, and evaluates the effectiveness of post-2008 financial crisis reforms, including the Dodd-Frank Act and European Market Infrastructure Regulation (EMIR). The paper addresses critical issues in OTC markets, including cross-border transactions, transparency requirements, central clearing mechanisms, and systemic risk management. By synthesizing legal, financial, and regulatory perspectives, this study offers a nuanced understanding of the evolving regulatory landscape. It identifies persistent challenges, such as the balance between innovation and risk mitigation, the impact of regulatory arbitrage, and the difficulties in achieving global regulatory harmonization. The research also explores emerging trends, including the influence of financial technology on OTC markets and the potential implications of sustainable finance initiatives. Ultimately, this paper aims to contribute to the ongoing dialogue on regulatory efficacy and proposes potential directions for future regulatory efforts, emphasizing the need for adaptive, principle-based approaches to keep pace with financial innovation while ensuring market stability and integrity.
Indian Journal of Economics and Finance, 2025
This paper presents a comprehensive analysis of callable bond pricing using the Hull-White intere... more This paper presents a comprehensive analysis of callable bond pricing using the Hull-White interest rate model. We compare the performance of the Hull-White model with other short-rate models, namely the Vasicek and Cox-Ingersoll-Ross (CIR) models, in pricing callable bonds across various market conditions. Through extensive numerical simulations and empirical analysis, we demonstrate that the Hull-White model generally outperforms other models in fitting the initial term structure and pricing callable bonds, particularly in volatile interest rate environments. We also propose an improved parameter estimation method for the Hull-White model, which enhances its pricing accuracy. Furthermore, we explore the
implications of using the Hull-White model for callable bond pricing on issuer and investor behavior. Our findings contribute to the literature on interest rate modeling and provide practical guidelines for financial professionals in the valuation and risk management of callable bonds.
Indian Journal of Law and Legal Research, 2024
In this paper, I examine the application of game theory to legal disputes over financial contract... more In this paper, I examine the application of game theory to legal disputes over financial contracts, offering a novel perspective on the strategic interactions that shape litigation outcomes in the financial sector. By leveraging game-theoretic models, I analyze the decision-making processes of parties involved in contract disputes, including plaintiffs, defendants, and judiciary bodies. My research explores how these models can be used to predict litigation strategies, inform settlement negotiations, and anticipate judicial decisions in the context of complex financial agreements.
I begin by establishing a theoretical framework that bridges game theory concepts with legal principles relevant to financial contract disputes. I then apply this framework to various scenarios, including securities fraud litigation and derivative contract conflicts, demonstrating how game theory can provide insights into the behavior of rational actors under conditions of uncertainty and information asymmetry.
My findings suggest that game theory offers a valuable analytical tool for understanding and navigating the intricacies of legal conflicts in the financial domain. By illuminating the strategic underpinnings of dispute resolution, this research contributes to both academic discourse and practical applications in law and finance. I conclude by discussing the implications of my analysis for legal practitioners, policymakers, and researchers, while also acknowledging the limitations of game-theoretic approaches and proposing avenues for future research.
Journal of Information Systems Engineering and Management, 2025
Artificial Intelligence (AI) is revolutionizing business operations and supply chain management b... more Artificial Intelligence (AI) is revolutionizing business operations and supply chain management by enhancing efficiency, reducing costs, and optimizing decision-making. This paper explores the transformative impact of AI in streamlining supply chain processes, improving demand forecasting, and enhancing inventory management. AI-driven automation, predictive analytics, and real-time data processing are enabling businesses to minimize inefficiencies, mitigate risks, and enhance overall productivity. The study highlights key AI technologies, including machine learning, natural language processing, and robotic process automation, which are reshaping logistics, procurement, and distribution networks. AI-powered solutions enable organizations to make data-driven decisions, enhance supplier collaboration, and improve customer satisfaction. Additionally, AI facilitates real-time monitoring of supply chains, providing actionable insights that help businesses proactively address disruptions and optimize resource allocation. Furthermore, this paper examines the role of AI in fostering sustainability by reducing waste, improving energy efficiency, and supporting ethical sourcing practices. While AI offers numerous benefits, its implementation also presents challenges such as data security concerns, high initial investment costs, and the need for skilled professionals. Addressing these challenges requires strategic integration, continuous innovation, and adherence to ethical guidelines. Through an extensive review of existing literature and case studies, this paper provides insights into the growing significance of AI in enhancing business efficiency and supply chain resilience. The findings emphasize the necessity for organizations to embrace AI-driven strategies to maintain a competitive edge in an increasingly dynamic and digitalized marketplace. Future research should focus on overcoming implementation barriers and exploring emerging AI applications that can further revolutionize supply chain management.
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Conference Presentations by Sarvagya Jha
and complexity of contracts in business transactions continue to rise exponentially, traditional manual review processes are rapidly becoming inefficient, expensive, and prone to human error. As a result, law firms and legal departments are seeking innovative solutions that not only
streamline workflows but also enhance accuracy and scalability.
This research explores how NLP technology can dramatically reduce the time and resources required for contract review and due diligence while improving accuracy and consistency. We analyze the current state of NLP in legal applications, examining innovations such as machine learning algorithms for contract classification, entity extraction for key clause
identification, and semantic analysis for understanding contract language (Surden, 2019a).
However, the integration of NLP into legal practice is not without challenges. We address critical concerns including data privacy, maintenance of attorney-client privilege, and the need for human oversight in legal decision-making (Chagal-Feferkorn, 2019). The paper also discusses the implications for legal education and practice, as successful implementation of NLP systems necessitates new skills and approaches from legal professionals (McGinnis & Pearce, 2014)
Papers by Sarvagya Jha
However, challenges related to cybersecurity, data privacy, and integration complexities remain key concerns. This study contributes to both academic and industry discussions by offering insights into the evolving role of AI and IoT in supply chains. Practically, it provides guidance for
businesses seeking to implement intelligent supply chain solutions. Socially, the findings emphasize the need for ethical AI deployment, data security, and workforce upskilling to navigate the shift toward autonomous operations. This research presents a unique synthesis of AI and IoT advancements in supply chain management, highlighting their synergistic impact on creating more adaptive, resilient, and intelligent operations. By addressing both opportunities and challenges, it serves as a valuable resource for researchers, practitioners, and policymakers in the field.
Keywords: AI in supply chains, IoT in logistics, intelligent supply chains, autonomous business operations, supply chain automation, digital transformation, Industry 4.0.
implications of using the Hull-White model for callable bond pricing on issuer and investor behavior. Our findings contribute to the literature on interest rate modeling and provide practical guidelines for financial professionals in the valuation and risk management of callable bonds.
I begin by establishing a theoretical framework that bridges game theory concepts with legal principles relevant to financial contract disputes. I then apply this framework to various scenarios, including securities fraud litigation and derivative contract conflicts, demonstrating how game theory can provide insights into the behavior of rational actors under conditions of uncertainty and information asymmetry.
My findings suggest that game theory offers a valuable analytical tool for understanding and navigating the intricacies of legal conflicts in the financial domain. By illuminating the strategic underpinnings of dispute resolution, this research contributes to both academic discourse and practical applications in law and finance. I conclude by discussing the implications of my analysis for legal practitioners, policymakers, and researchers, while also acknowledging the limitations of game-theoretic approaches and proposing avenues for future research.
and complexity of contracts in business transactions continue to rise exponentially, traditional manual review processes are rapidly becoming inefficient, expensive, and prone to human error. As a result, law firms and legal departments are seeking innovative solutions that not only
streamline workflows but also enhance accuracy and scalability.
This research explores how NLP technology can dramatically reduce the time and resources required for contract review and due diligence while improving accuracy and consistency. We analyze the current state of NLP in legal applications, examining innovations such as machine learning algorithms for contract classification, entity extraction for key clause
identification, and semantic analysis for understanding contract language (Surden, 2019a).
However, the integration of NLP into legal practice is not without challenges. We address critical concerns including data privacy, maintenance of attorney-client privilege, and the need for human oversight in legal decision-making (Chagal-Feferkorn, 2019). The paper also discusses the implications for legal education and practice, as successful implementation of NLP systems necessitates new skills and approaches from legal professionals (McGinnis & Pearce, 2014)
However, challenges related to cybersecurity, data privacy, and integration complexities remain key concerns. This study contributes to both academic and industry discussions by offering insights into the evolving role of AI and IoT in supply chains. Practically, it provides guidance for
businesses seeking to implement intelligent supply chain solutions. Socially, the findings emphasize the need for ethical AI deployment, data security, and workforce upskilling to navigate the shift toward autonomous operations. This research presents a unique synthesis of AI and IoT advancements in supply chain management, highlighting their synergistic impact on creating more adaptive, resilient, and intelligent operations. By addressing both opportunities and challenges, it serves as a valuable resource for researchers, practitioners, and policymakers in the field.
Keywords: AI in supply chains, IoT in logistics, intelligent supply chains, autonomous business operations, supply chain automation, digital transformation, Industry 4.0.
implications of using the Hull-White model for callable bond pricing on issuer and investor behavior. Our findings contribute to the literature on interest rate modeling and provide practical guidelines for financial professionals in the valuation and risk management of callable bonds.
I begin by establishing a theoretical framework that bridges game theory concepts with legal principles relevant to financial contract disputes. I then apply this framework to various scenarios, including securities fraud litigation and derivative contract conflicts, demonstrating how game theory can provide insights into the behavior of rational actors under conditions of uncertainty and information asymmetry.
My findings suggest that game theory offers a valuable analytical tool for understanding and navigating the intricacies of legal conflicts in the financial domain. By illuminating the strategic underpinnings of dispute resolution, this research contributes to both academic discourse and practical applications in law and finance. I conclude by discussing the implications of my analysis for legal practitioners, policymakers, and researchers, while also acknowledging the limitations of game-theoretic approaches and proposing avenues for future research.