The retail industry faces increasing challenges in matching supply with demand due to evolving consumer behaviors, market volatility, and supply chain disruptions. While existing approaches employ statistical and machine learning methods for demand forecasting, they often fail to capture complex temporal dependencies and lack the ability to simultaneously optimize inventory decisions. This paper proposes a novel multi-agent deep reinforcement learning framework that jointly optimizes demand forecasting and inventory management in retail supply chains, leveraging data from IoT sensors, RFID tracking systems, and smart shelf monitoring devices. Our approach combines transformer-based sequence modeling for demand patterns with hierarchical reinforcement learning agents that coordinate inventory decisions across distribution networks. The framework integrates both historical sales data and real-time sensor measurements, employing attention mechanisms to capture seasonal patterns, promotional effects, and environmental conditions detected through temperature and humidity sensors. Through extensive experiments on large-scale retail datasets incorporating sensor network data, we demonstrate that our method achieves 18.2% lower forecast error and 23.5% reduced stockout rates compared with state-of-the-art baselines. The results show particular improvements in handling promotional events and seasonal transitions, where traditional methods often struggle. Our work provides new insights into leveraging deep reinforcement learning for integrated retail operations optimization and offers a scalable solution for modern sensor-enabled supply chain challenges.

With the proliferation of mobile devices and payment systems in modern financial services, there is an increasing need to process and analyze continuous streams of transaction data for credit risk assessment. Leveraging the inherent symmetries in financial markets and data structures, this paper introduces DeepCreditRisk, a symmetry-aware deep learning framework that addresses key challenges while maintaining critical invariance properties in financial data representation. The framework incorporates three main components: an adaptive temporal fusion mechanism, a heterogeneous graph neural network, and an attention-based interpretable output layer. The temporal fusion mechanism effectively models both short-term fluctuations and long-term trends in financial time series, while the heterogeneous graph neural network captures intricate relationships within the financial ecosystem. The framework maintains important symmetrical properties in both temporal and structural representations, ensuring balanced feature learning and invariant risk assessment. The attention-based output layer preserves representation symmetry while enhancing model interpretability. Extensive experiments on a large-scale credit risk dataset demonstrate DeepCreditRisk’s superior performance, achieving a 7.2% improvement in the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) and an 18.6% improvement in the Kolmogorov–Smirnov (KS) statistic over state-of-the-art baseline models. The framework maintains high predictive power across various time horizons and provides interpretable insights into feature importance. DeepCreditRisk represents a significant advancement in applying deep learning to credit risk assessment, offering financial institutions a more accurate, robust, and transparent approach for evaluating creditworthiness and managing risk.

The use of artificial intelligence (AI) in accounting and finance is reshaping how organizations ensure accuracy, detect fraud, and maintain transparency in their financial operations. This paper reviews how AI-driven technologies-particularly machine learning (ML), deep learning (DL), and natural language processing (NLP)-are being applied to modern accounting systems. We discuss how these tools enhance financial accuracy by automating data processing, identifying anomalies in real time, and correcting errors intelligently. Advanced fraud detection systems based on supervised and unsupervised learning, neural networks, and ensemble methods are shown to recognize suspicious transactions and accounting irregularities with remarkable precision. The paper also explores how AI supports transparency through automated compliance checks, smart auditing systems, and blockchain-based solutions that build trust and accountability. In addition, we highlight recent developments in predictive analytics for financial forecasting, robotic process automation (RPA) in accounting workflows, and explainable AI (XAI) for regulatory compliance. Key implementation challenges are addressed, including data quality, algorithmic bias, model interpretability, and evolving regulatory frameworks. The review further considers how AI integrates with enterprise resource planning (ERP) systems, safeguards sensitive financial data, and raises new ethical questions around automation and human oversight. Finally, we identify emerging directions such as federated learning for cross-organization fraud detection, graph neural networks for analyzing complex transaction patterns, and hybrid human-AI collaboration models. These advancements point toward a future where continuous auditing, multimodal financial analysis, and AI-driven regulatory technologies transform the landscape of accounting and financial management.

Large language models (LLMs) have emerged as transformative technologies in financial services, demonstrating unprecedented capabilities in extracting structured knowledge from unstructured financial documents, generating analytical insights, and supporting strategic corporate planning decisions. This review paper examines the comprehensive applications of LLMs including GPT-4, Claude, PaLM, and domain-specific financial models in automating knowledge extraction from diverse sources including earnings calls, financial reports, regulatory filings, and market commentary. We analyze how transformer-based architectures (TA) leverage attention mechanisms and contextual embeddings to understand complex financial terminology, temporal relationships, and causal connections in financial narratives. The paper explores advanced techniques including prompt engineering, few-shot learning, retrieval-augmented generation (RAG), and fine-tuning strategies that adapt general-purpose LLMs to specialized financial tasks. We examine applications in sentiment analysis of financial texts, automatic summarization of lengthy reports, entity recognition for companies and products, relationship extraction between financial events, and question-answering systems for financial queries. The review investigates how LLMs generate analytical insights through scenario analysis, trend identification, risk assessment, and competitive intelligence synthesis. We analyze corporate planning support applications including strategic initiative identification, market opportunity analysis, resource allocation recommendations, and investment thesis generation. Furthermore, we discuss integration architectures combining LLMs with structured databases, time-series models, and visualization tools to create comprehensive decision support systems. The paper addresses critical challenges including hallucination mitigation, accuracy verification, regulatory compliance, data privacy concerns, and the need for human oversight in high-stakes financial decisions. We examine evaluation methodologies for financial LLM applications, including domain-specific benchmarks, expert assessment protocols, and real-world performance metrics. Through synthesis of current research and deployed systems, we identify limitations including computational costs, update frequency challenges, bias in training data, and difficulties in explaining model reasoning. The review concludes by outlining promising research directions including multimodal financial analysis, real-time information integration, federated learning for privacy-preserving collaboration, and neuro-symbolic approaches combining neural language understanding with formal financial reasoning.

The insurance industry faces mounting pressure to modernize claims management processes amid rising claim volumes, increasing operational costs, and evolving customer expectations. Traditional manual claims processing systems suffer from inefficiencies, high error rates, and extended processing times that negatively impact both operational performance and customer satisfaction. Artificial intelligence (AI) and automation technologies have emerged as transformative solutions, offering unprecedented capabilities to streamline claims workflows, enhance decision accuracy, and improve customer experiences. This comprehensive review examines the application of AI and automation in insurance claims management, analyzing research published between 2019 and 2025. We explore diverse AI technologies including machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, and robotic process automation (RPA) across various insurance domains. The review reveals that AI-powered systems achieve substantial improvements in processing efficiency, with automation reducing processing times by up to 80% while maintaining or improving accuracy. Deep learning architectures demonstrate particular effectiveness in complex tasks such as fraud detection, damage assessment, and claim severity prediction. However, significant challenges persist, including data quality concerns, integration complexities with legacy systems, model interpretability requirements, and ethical considerations regarding algorithmic decision-making. We identify emerging trends including explainable AI frameworks, federated learning for privacy preservation, and hybrid human-AI collaboration models. This review contributes to understanding the current state and future trajectory of intelligent claims management systems while highlighting critical implementation considerations for insurance organizations.