Progressive Corporation

This study delves into applying blockchain technology to maximize trust and efficiency when it comes to the process of verifying insurance claims, in health and motor car insurance markets in this case. Through the application of a permissioned blockchain network, the study proves how blockchain transparency and decentralization can facilitate processing claims yet maintain data integrity and minimize fraud. Four core algorithms—Blockchain Consensus, Smart Contracts, Fraud Detection, and Claim Validation—were developed and implemented. The tests registered a reduction of 30

This study examines how generative AI models can be utilized for the process optimization of the semiconductor wafer design and predict the yield leading to the semiconductor wafer design. The study pays attention to the use of deep learning algorithms, such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and reinforcement learning, to advance semiconductor manufacturing efficiency, precision, and yield. A number of AI methods were used to optimize the wafer design, identify the malfunctions, and calculate the yield according to the process parameters. As the result showed, the AI-driven models were better at the defect detection and the yield prediction than the traditional approaches, as the difference in the accuracy of the established models compared to the conventional models constituted 15

The use of generative artificial intelligence (GenAI) by enterprises is growing faster in the regulated sectors like insurance and public financial management (PFM). Nevertheless, its practical implementation is difficult because of strict conditions related to the security of the data, meeting the requirements provided by the regulations, audits, and governance. This paper includes the design, implementation, and assessment of a generative AI agent framework of enterprise scale, providing secured and regulated automation of insurance and PFM processes. The suggested system will combine large language models (LLMs) with policyorchestrated large language models, role-based data access, audit logging, and human-in-the-loop controls. The framework is applied and checked on real world applications such as insurance claims processing and government financial reporting. As the experiments conducted reveal, the efficiency of the processes, a smaller manual workload, and complete compliance traceability are all improved, which is why the solution can be used in the regulated enterprise setting.

The organizations working across multiple cloud services encounter many difficulties that impact the integration and reasoning of different sources of heterogeneous data. These hurdles include the interoperability, scalability, and compliance of these systems. To improve upon the current limitations, this research proposes a cloud-independent orchestration protocol based on a Hierarchical Agentic RAG Framework. The framework includes a multi-layered agent structure consisting of agents such as decomposing agents, retrieval agents, and synthesis agents, which work together to enable a more efficient method for reasoning through multiple hops of data regardless of the source (i.e., structured, semi-structured, or unstructured) from within the organization. The proposed protocol allows for the decoupling of the reasoning workflow from the dependencies on any specific cloud platform, which allows for flexibility in the ongoing changes that may happen with any of the service providers that an organization may utilize. The experimental evaluation presented demonstrates that the framework has an accuracy of 93% while outperforming traditional RAG models and existing, cloud-dependent orchestration protocols, in terms of precision, recall, and F1 Score. Furthermore, the system has demonstrated strong levels of scalability, interoperability, and adaptability within a very complex enterprise environment. The proposed protocol is especially applicable in operations/organizations where data governance and cross-cloud intelligence will be necessary; such as finance, healthcare, and government. Collectively, this research provides a reliable, scalable, and efficient means for enterprise knowledge orchestration and accelerating reasoning processes throughout the distributed cloud ecosystem.

Smart manufacturing offers an increasingly integrated and data-driven approach to production, benefitting from the latest technologies like Industry 4.0, Internet of Things (IoT), cloud computing, and Big Data. Real-time artificial intelligence (AI)-enabled auditing and compliance can exploit these technologies to manage compliance in real-time. The advantages of continuous auditing compared with batch auditing are obvious. AI supports the automation of many audit and compliance functions. Although companies have adopted artificial intelligence for various operations, the AI model development process requires considerable resources, time, and expertise. Therefore, the AI-supported real-time audit and compliance framework is within smart manufacturing finance. Key definitions are clarified: Real-Time Audit (RTA), Continuous Controls Monitoring (CCM), AI components (models, data preparation), data lineage, and governance terms. A Continuous Auditing and Continuous Controls Monitoring (CACM) model supports more efficient compliance management and verification. Information Technology Controls and General Application Controls aligned with common regulations can now be automated. Real-time AI-driven event detection, triggered event correlation, preventive controls, and data-driven tests enhance the framework. Control objectives aligned with mother regulations support validation and assurance. Updated AI model governance principles allow support for production without expertise, acquiring test data during the normal course of business. As datasets accumulate, AI can evolve to operate with minimal specification. With present advances in AI and data-and-compute-storage cloud solutions, the necessary compute and storage resources are available to combine operation and audit seamlessly.