The rapid adoption of Artificial Intelligence (AI) in Supply Chain Management (SCM) has transformed traditional decision-making processes by enabling predictive analytics, demand forecasting, inventory optimization, supplier evaluation, logistics planning, and risk management. Despite the significant performance improvements achieved through AI-driven systems, the increasing reliance on complex machine learning and deep learning models has raised concerns regarding transparency, trustworthiness, accountability, and regulatory compliance. Many advanced AI models operate as “black boxes,” making it difficult for supply chain managers and stakeholders to understand how decisions are generated. Consequently, Explainable Artificial Intelligence (XAI) has emerged as a promising solution to enhance transparency and interpretability while maintaining the predictive power of AI systems. This paper presents a comprehensive review and conceptual analysis of Explainable AI in Supply Chain Management, examining its opportunities, applications, challenges, and future research directions. The study synthesizes findings from recent literature published between 2018 and 2026 and critically evaluates the role of XAI across major supply chain functions, including demand forecasting, procurement, supplier selection, inventory management, transportation, sustainability monitoring, and risk mitigation. The paper further identifies key barriers to XAI implementation, including data quality issues, model complexity, scalability concerns, organizational resistance, and the lack of standardized evaluation frameworks. A conceptual framework is proposed to illustrate how explainability can improve decision quality, stakeholder trust, operational resilience, and sustainable supply chain performance. The findings indicate that organizations integrating explainable AI mechanisms into supply chain analytics are better positioned to achieve transparency, regulatory compliance, and collaborative decision-making. The paper contributes to both theory and practice by establishing a research agenda that addresses emerging technological, managerial, and sustainability-related challenges associated with Explainable AI adoption in modern supply chains.
Keywords: Explainable Artificial Intelligence (XAI), Supply Chain Management, Machine Learning, Decision Support Systems, Supply Chain Analytics, Transparency, Sustainable Supply Chains
Pragadeesh Roopchander· International Journal of Cre...· 0 citations
The insurance industry is undergoing a profound digital transformation driven by advances in machine learning (ML), artificial intelligence (AI), big data analytics, and cloud computing. Traditional claim prediction models, which primarily rely on demographic and historical claim information, often struggle to capture the complex behavioral patterns and dynamic risk factors that influence claim occurrence and severity. Recent developments in customer analytics, telematics, Internet of Things (IoT) devices, wearable technologies, and digital customer interactions have created opportunities to develop more accurate, adaptive, and explainable predictive models. This paper presents a comprehensive conceptual analysis of machine learning-based insurance claim prediction using customer behavioral and policy data. Drawing upon recent research published between 2018 and 2026, the study critically examines the evolution of predictive techniques, including Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, Gradient Boosting, Extreme Gradient Boosting (XGBoost), LightGBM, CatBoost, Artificial Neural Networks, and Deep Learning architectures. Beyond algorithmic performance, the paper evaluates emerging issues such as data quality, class imbalance, model interpretability, algorithmic fairness, privacy preservation, regulatory compliance, and deployment challenges in real-world insurance environments. A conceptual framework is proposed to illustrate the integration of customer behavioral characteristics, policy attributes, and advanced machine learning models into an intelligent decision-support system for claim prediction. The framework highlights the importance of explainable AI, continuous model monitoring, and human-in-the-loop decision-making to enhance transparency and operational trust. The study contributes to the literature by synthesizing recent developments, identifying critical research gaps, and outlining future research directions involving federated learning, graph neural networks, multimodal data fusion, causal machine learning, and generative AI. The findings provide valuable insights for researchers, insurance practitioners, and policymakers seeking to improve underwriting accuracy, fraud detection, claims management, and customer-centric risk assessment while supporting sustainable and data-driven insurance ecosystems. Keywords: Machine Learning, Insurance Claim Prediction, Customer Behavioral Analytics, Policy Data, Explainable Artificial Intelligence (XAI), Risk Assessment, Predictive Analytics
Pragadeesh Roopchander· International Journal of Cre...· 0 citations
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