The human brain's development is a complex and highly orchestrated process influenced by a myriad of genetic factors. Disruptions in these processes can lead to various neuropsychiatric disorders. This study presents an integrative functional genomic analysis aimed at elucidating the genetic underpinnings of human brain development and their associations with neuropsychiatric risks. Utilizing advanced genomic technologies, including whole-genome sequencing, transcriptomics, and epigenomics, we identify key regulatory networks and genetic variants implicated in neurodevelopment. By integrating data from diverse cohorts and employing sophisticated bioinformatics tools, we map the interplay between genetic predispositions and environmental factors in the etiology of neuropsychiatric conditions. Our findings highlight novel genetic markers and pathways that may serve as potential targets for therapeutic interventions. This comprehensive analysis enhances our understanding of the genetic architecture of brain development and provides a foundation for future research into the prevention and treatment of neuropsychiatric disorders.

Performance testing stands as a crucial procedure that verifies the scalability and reliability aspects of distributed systems while specifically enhancing the efficiency of microservices architectures. The demand for faster application communication protocols in modern systems has led to the widespread adoption of GRPC because of its ability to deliver low-latency and high-performance remote procedure call services. Researchers in this paper demonstrate how Apache JMeter and Gatling performance testing tools can evaluate GRPC services. Our goal is to examine how GRPC manages different traffic patterns by performing load testing alongside spike testing, endurance testing and stress testing to assess its performance features. These findings provide essential guidance for developers who want to enhance their services for production-level deployment while achieving reliability under real-world conditions.

As the health insurance industry digitizes at a rapid pace, data engineering and analytics are upheld within the industry as indispensable tools for better policies and claims service operations along with more effective compliance management. This article illustrates the problems that data engineers and analysts must solve so as to ease the operation of health insurance. Securing heterotic sources of information can be interfaced with illumination filters. The computing of work queues will become a thing heretofore poorly conceived. It is possible to find out Overflows and make them disappear. And that approach leads to decision- making optimization. In particular, responsibilities include debug requests from 587s, model data flows, clean datasets, and run production automatic-jobs as well as coordinating deployment. Nor can health insurance providers manage the policy changes. How can they do so when this takes more time, indeed very many cycles longer than ever before? So how do they adapt? As health insurers offering customers with services in a data-driven era networks and insurers of alliances among stakeholders do better. In education organization for this type of world–is needed too. For insurance market today and tomorrow, life insurance companies are already starting to face innovation and change: data own technologies, long-term health goal setting, early warning fragmented experience reconstruction of medical practices industry has brought us. It is these information-based systems that will change how people bought life policies next year.

This paper examines the usage of machine learning models in forecasting performance and retention among employees, important organizational performance elements. Both substandard performance and high turnover are expensive, and in turn, insights based on data are a requirement. The research applies a comprehensive literature review and examines existing literature and finds predictors such as satisfaction, length of service, compensation, and engagement. It establishes a predictive model-building process to efficiently forecast these outcomes. The research establishes such models allow firms to proactively choose, allocate resources in a productive way, and lower costs on turnover. Data privacy, interpretability, and bias are however among the implementation barriers. The paper concludes with a mention on machine learning’s potential in revolutionizing HR analytics, with a systematic process in utilizing insights ethically. It supports future research in ethically aligned AI and real-time predictions and makes a useful contribution in workforce strategy.

This paper presents a comprehensive benchmark and survey of fully unsupervised concept drift detectors (UCDD) designed to identify and adapt to concept drift in real-world data streams. Concept drift refers to the phenomenon where the statistical properties of a data stream change over time, leading to the deterioration of model accuracy if not detected and adjusted. The study reviews the state of the art in UCDDs, evaluates their performance on various real-world datasets, and identifies challenges and open research areas in the field. Through empirical experiments and a systematic review of existing methods, we highlight key factors influencing the performance of these detectors in unsupervised environments.

Anomaly detection plays a pivotal role in data-driven machine learning applications, enabling the identification of rare or unexpected patterns that deviate from the norm. These anomalies, which can indicate critical events such as fraud, security breaches, equipment failures, or medical conditions, are invaluable in a variety of fields. This paper provides an in-depth review of anomaly analytics, focusing on the various techniques used in machine learning to detect anomalies in complex, high-dimensional data. We explore statistical methods, machine learning-based approaches, and hybrid models, analyzing their strengths and weaknesses across multiple domains including cybersecurity, finance, healthcare, and manufacturing. The paper also discusses key evaluation metrics for anomaly detection and highlights the challenges of scalability, noise handling, and model interpretability. Finally, we examine emerging trends in anomaly detection, including real-time processing and explainability, and suggest future research directions to improve the robustness and efficiency of anomaly detection systems in large-scale, dynamic environments. This work serves as a comprehensive guide for understanding the role of anomaly analytics in modern machine learning applications, offering insights into current methodologies and future advancements.

Adversarial attacks on time series data have gained increasing attention due to their potential to undermine the robustness of machine learning models. These attacks often manipulate input data with the goal of causing misclassification, misprediction, or degradation of model performance. This paper investigates time series adversarial attacks, focusing on smooth perturbations that are difficult to detect. We explore the characteristics of these smooth perturbations and review various defense approaches designed to mitigate their impact. Our analysis highlights the challenges and potential solutions in enhancing the robustness of time series models against adversarial threats.

Modern day Supply chain and logistics management system integrates artificial intelligence (AI) and machine learning (ML) to develop it into an operational transformation which enhances resilience, reduces costs and improves efficiency in corporate offices. This paper evaluates how artificial intelligence and machine learning-based demand forecasting and route optimization systems facilitate process optimization through inventory management. This paper applies to NYX as an example of a mid-sized logistics manufacturer to present real-world applications of these technologies and extract important implementation lessons. The success of predictive analytics combined with artificial intelligence depends on solving data combination and operation scale maintenance issues so it can enhance logistics efficiency through machine learning optimization algorithms. Organizations interested in supply chain modernization can utilize the discovered findings that implementing AI and ML strategically results in substantial operational benefits.

In the Artificial Intelligence and Machine Learning Era, the insurance industry is undergoing a rapid transformation. The integration of automated technologies enhances the efficiency and customer satisfaction of insurers. One of those advancements is the execution of an automated insurance quoting system. This review paper highlights the features, benefits, and challenges of these systems, utilizing industry insights. Further, this paper will discuss the evolution of the quoting process in the insurance world and will explain the future view on AI and automation trends.

Extract, Transform, Load (ETL) processes form the backbone of data manage- ment and consolidation in today’s data-driven enterprises with prevalent large- scale data pipelines. One of the widely used ETL tools is Microsoft SQL Server Integration Services (SSIS), yet its optimization for performance for large-scale data loads remains a challenge. As the volumes of data grow exponentially, inefficient ETL processes create bottlenecks, increased processing time, and ex- haustion of system resources. This work discusses major SSIS optimizations that minimize ETL processing time, allowing for effective and scalable data integration.

One of the key areas of optimization is data flow optimization, such as lever- aging the use of the Fast Load mode in OLE DB Destination to perform batch inserts instead of row-by-row. Similarly, Bulk Insert operations can signifi- cantly reduce data movement time. Additionally, buffer size and DefaultBuffer- MaxRows tuning allows SSIS to process data in memory more efficiently, thereby minimizing disk I/O operations.

Another major area of focus is source query optimization. With the utiliza- tion of indexed views, partitioned tables, and filtering in the WHERE clause, unnecessary data extraction is avoided, restricting the load on the source sys- tem. NOLOCK hints also minimize database contention in high-concurrency environments. Parallel execution of multiple operations within SSIS can also accelerate execution, with multithreading and batch processing enabling con- current data conversion.

Lookup transformations, a common performance bottleneck, can be opti- mized using cache mode, where reference data is pre-loaded instead of querying the database for each row. Furthermore, replacing row-based transformations with set-based operations significantly reduces processing overhead.

For incremental data loading, change tracking or CDC (Change Data Cap- ture) enables altered record processing in place of full set loads. This saves time in processing and optimizes utilization of resources.  ETL logging and error-

handling mechanisms play an important role as well; selective SSIS logging and event-based error-handling mechanisms can prevent performance degradation due to overlogging.

Lastly, SSIS package configurations can be tuned by having proper indexing of destination tables, turning off unnecessary constraints during loading, and applying table partitioning to maximize parallel loads of data.

By utilizing these SSIS optimizations, organizations can reduce ETL pro- cessing by significant quantities, optimize data pipelines, and overall enhance enterprise-level data integration performance. These approaches make large- scale big-data scale data pipelines have very low latency, thus making SSIS a more efficient and scalable solution for enterprise-level data workflows.

With the rapid development of financial services, recording and data aggregation need to be efficient so information from varying record keepers (banks, custodians, pension administrators) can be aggregated. The downside of various data formats in disparate datasets coming together into one unified place for the sake of being in one place is glaring in the data format, standards of how it will be reported, and lack of metadata. Inaccuracies, timeliness, and unreliability cause financial data to threaten business operations and compliance requirements. Based on financial datasets, it frames an aggregation framework for producing a scalable, standardized, and improved data quality aggregated dataset. Such data quality can be addressed by modular architecture, real-time validation, and centralized monitoring provided by the architecture.Using grace with the metadata-driven rule handling and automation via ETL pipelines to guarantee integrity and compliance with data, the framework also leverages the framework. A case study of a multi-manager pension platform using the proposed framework is further demonstrated, leading to improved data consistency, reporting timeliness, and reduction of reconciliation errors. The paper ends by discussing ethical issues, explaining how to practice the framework, and looking at two future trends employing AI for predictive error models, blockchain for data lineage and audibility, and how regulators can use RegTech to automate the reporting process with compliance. Considering all this, the above-proposed framework provides the perfect overall solution for financial institutions, fintech platforms, and asset managers to make the operation more efficient and build trust between financial data in the industry.

Regulatory compliance is increasingly a fundamental part of a methodology to shield one’s organization from unscrupulous practices in enterprise IT. Organizations are bound by these compliance frameworks, such as GDPR, SOC 2, and the Health Insurance Portability and Accountability Act (HIPAA), to have the most potent data security, privacy, and integrity controls in place as they pertain to data. Organizations can get integrated options for handling workflows and ensuring compliance with the automated options of IT Service Management (ITSM) tools like Jira. With customizable workflows, automated notifications, and task assignments, Jira exposes organizations to powerful and easy-to-enforce compliance with these regulations across large and distributed teams. This study explores ways of automating the compliance workflows using Jira and how it would integrate well with other ITSM tools and perfectly tie with IT service and DevOps processes. It also talks about how complex it is to automate compliance, including configuring workflows and integrating legacy systems. This will help the organization automate compliance tasks, lessen human error risk, accelerate the audit, and stay on track with compliance metrics. Jira case studies are also presented, which explain how Jira is used in high-risk cases, reducing the risk associated with compliance and improving audit and streamlining of workflow. The paper ends by recommending industry organizations that want to utilize the best practices of compliance automation as part of their strategies and predicting trends that will affect compliance automation ITSM practices in the future, including AI and machine learning, blockchain technology.

Manufacturing Execution Systems (MES) have evolved significantly over the past few decades, serving as a critical link between shop-floor operations and enterprise resource planning. Initially focused on reactive strategies—offering real-time visibility and control based on immediate conditions—MES have transitioned toward predictive capabilities driven by Industry 4.0 technologies. The integration of big data analytics, the Internet of Things (IoT), machine learning, and cloud computing has enabled autonomous factories to leverage MES for proactive and adaptive decision-making. This paper explores the transformation of MES from reactive to predictive systems, detailing the technological enablers, including IoT sensor networks, machine learning algorithms, digital twins, and cyber-physical systems. A methodology for designing and implementing a predictive MES architecture is presented, supported by empirical findings from a pilot implementation. Results demonstrate improvements in production efficiency, reduced downtime, and optimized resource use. Challenges such as data security, integration complexities, and workforce training are discussed, alongside future directions involving cognitive MES and AI-driven manufacturing. The paper also highlights environmental sustainability benefits, positioning predictive MES as a cornerstone of modern autonomous factories.

The financial services industry is transforming batch processing to real-time, AI-driven architectures. This article looks at how the frameworks Apache Kafka and Apache Spark are used as bases for building scalable and low-latency, fault-tolerant data pipelines, meeting the special requirements of the financial sector. These real-time applications include high-frequency trading, fraud detection, compliance monitoring, and customer engagement. They are made possible through these open-source platforms that publicly ingest, process, and make decisions. Integrating cloud-native infrastructure—using Kubernetes, service mesh, and container orchestration—ensures elasticity, security, and regulatory alignment. Large language models (LLMs) are now being entrenched into micro services for decision support, regulatory reporting automation, and the automation of client interactions. The article also contains detailed architectural guidance on how to integrate Kafka and Spark, tips for improving Kafka Spark performance, and best practices around observability and DevSecOps. Real-time stream processing combined with AI-driven analysis serves as a real-world use case for trade surveillance. The future impact of emerging trends such as edge-native computing, federated learning, and decentralized finance is also examined. Strategic recommendations to CTOs and architects for developing secure, AI-native, and future-proof financial systems are presented to close.

Current Good Manufacturing Practice (cGMP) regulations emphasize stringent control over production processes, personnel, equipment, and documentation in pharmaceutical manufacturing. Meeting cGMP requirements involves meticulous recordkeeping, comprehensive quality control, and robust oversight—processes that are prone to human error when relying on traditional, paper-based approaches. Against this backdrop, Manufacturing Execution Systems (MES) offer a powerful solution for managing production workflows and ensuring regulatory adherence. This paper explores the integration of MES in a cGMP environment to automate compliance assurance and details key strategies including automated validation, data integrity assurance, QMS integration, regulatory reporting, training and competency tracking, risk-based automation, and AI-driven continuous improvement. Through literature reviews and case study analyses, we identify critical process elements where MES adds the most value, such as reducing human error, streamlining documentation, and facilitating digital audit trails. The findings suggest that adopting MES not only enhances operational efficiency but also enables a proactive approach to regulatory compliance, positioning organizations to adapt quickly to evolving industry standards.

Serverless technologies have represented a significant advancement in cloud computing, characterized by its exceptional scalability and the granular subscription-based model provided by leading public cloud vendors. Concurrently, serverless platforms that facilitate the FaaS architecture enable users to use numerous benefits while functioning on the on-site infrastructures of enterprises. It makes it possible to install and use them on several tiers of the cloud-to-edge continuum, from IoT devices at the user end to on-site clusters near to the main sources or directly in the Cloud. The challenges caused by varying data input rates on low-powered gadgets at the user-end layers are addressed in this work in two ways. It offers an event-driven, open-source file handling system designed to dynamically distribute and rearrange serverless operations throughout the cloud-to-edge spectrum. A fire detection use case illustrates the efficacy of these techniques, utilizing small Kubernetes clusters at the Edge for Fog-level processing, on-premises elastic clusters for private cloud computing, and AWS Lambda for cloud computing execution. Findings demonstrate that coordinated multi-layer computing markedly diminishes system overload, hence improving performance in distributed cloud systems.

As high-performance computing becomes increasingly popular, graphics processing units (GPUS) are finding their place in multiple industries, such as gaming, artificial intelligence and data processing. With continued evolutionary changes in performance and complexity of GPUS, the issue of using scalable acoustic and thermal validation strategies to guarantee the reliability and efficiency of these devices has become a major challenge for manufacturers. This article discusses how important it is to have a linear approach to validation procedures for acoustic and thermal properties in the case of GPU production. Acoustic validation targets noise control, critical for user satisfaction in quiet operating environments. Thermal validation provides an ideal heat dissipation to prevent performance throttling and hardware degradation. Both factors greatly contribute to making GPUS faster, longer-lasting, and providing a better user experience. The article discusses current standards of verification, problems with scaling current strategies to mass production, and developing trends (e.g. the use of artificial intelligence and machine learning for predictive testing). It indicates the necessity for more sophisticated and convenient validation methods to fit the increased complexity and needs for GPUS. Manufacturers are encouraged to use innovative validation systems like AI-driven systems to enhance testing accuracy and reduce costs and production timelines. The article ends with a call to action that urges manufacturers to embrace scalable validation methods to guarantee further success and development of GPUS in an ever more competitive environment.

The integration of Enterprise Resource Planning (ERP) systems with Manufacturing Information Scanning Systems is essential for establishing a robust infrastructure capable of supporting high-quality big data flows. This foundational integration enables more sophisticated analytical modeling, leading to enhanced decision-making capabilities and effective incorporation of Artificial Intelligence supply chain operations, ultimately driving cost optimization.

Key findings of this research highlight that successful implementation of integrated ERP solutions extends beyond technical complexities; it critically depends on management's strategic decision-making at each implementation stage. Effective integration contributes significantly to improved operational efficiency, stronger customer relationship management, and more accurate accounting processes. However, substantial challenges persist, particularly related to the complexities of migrating historical data from legacy systems such as MS Access to modern ERP systems Additionally, organizations face ongoing data management issues and significant organizational resistance toward developing and sustaining a data-driven culture

This paper explores these challenges in-depth, presenting strategic insights and practical methodologies for organizations to successfully integrate ERP and Manufacturing Information Systems. By overcoming the highlighted barriers, organizations can fully leverage their integrated ERP systems, unlock comprehensive analytical capabilities, and achieve substantial cost optimization within supply chain management.

Managing a fleet of autonomous vehicles (AVs) efficiently is crucial for keeping them running smoothly and safely. In this paper, we present a Fleet Management System (FMS) that uses data analytics and AI to help fleet managers monitor vehicle performance, predict maintenance needs, and optimize operations. The system continuously collects data from various vehicle sensors and processes it to detect issues like low fuel, battery health, or ADAS faults. It also makes safety recommendations, predicts when vehicles need maintenance, and helps decide the best routes for each vehicle. By combining real-time monitoring with AI-driven decision-making, this system improves safety, reduces downtime, and enhances overall fleet efficiency. We explore how this AI-based approach can transform fleet management and provide a solid foundation for future advancements in autonomous vehicle operations.

The acceptance of LiDAR (Light Detection and Ranging) technology in self-driven vehicles and urban mapping is substantial. In the auto insurance domain, LiDAR’s accurate depth-sensing potential proposes its unexploited opportunity, which can help insurers tremendously. This review paper examines the current use of LiDAR and prospective applications in auto insurance in areas like risk assessment, claim settlements, fraud detection, and driver behavior analysis. We will look into the technological underpinning of LiDAR and its integration challenges, and put forward a hypothetical framework for its acquisition in Insurance processing steps. In conclusion, this paper proposes future research areas and the tactical role of technologies like cloud and AI in implementing LiDAR-collected data in the insurance world.

This work identifies and analyzes callback service architectures for high throughput, cloud-native applications. Like anyone who has worked in banking, insurance, or virtualization, microservices can suffer from the same problems and become event-driven without awareness. Callback mechanisms are now a key enabler for distributed systems' responsiveness, scalability, and fault tolerance. In this paper, we compare the efficiency of callbacks and polling methods and show that callbacks reduce latency and have a lower resource overhead. Webhooks, message queue subscribers (e.g., Kafka, RabbitMQ, AWS SQS), and gRPC streams are examined as core architectural patterns. The paper shows how use cases such as real-time transaction alerts, insurance claim updates, and high-frequency trading notifications can be executed more efficiently with callback-driven designs to ensure system responsiveness. In-depth analysis of similar yet different problems such as retry storms, latency bottlenecks, impotence handling, and backpressure vulnerabilities. To confront these issues, the study suggests design approaches like Circuit Breakers, Stateless scaling, Centralized retry orchestration, and Observability with the help of tools like Open Telemetry. The research further shows how callbacks facilitate the use of multi-protocol delivery mechanisms—HTTP, SMTP, and AWS SNS—essential in real-world microservices ecosystems. Measurable latency, fault tolerance, and operational cost improvements are shown in a case study involving the transition from monolithic synchronous designs to decoupled serverless architectures using AWS Lambda and SNS. This paper provides a practical reference model for building robust, callback-oriented systems, combining literature review, industry insights, simulations, and expert interviews. The results provide valuable guidance for system architects and DevOps engineers looking to build scalable, resilient, real-time service architectures.

As insurers are increasingly utilizing artificial intelligence for underwriting, pricing, and claims processing in an automated manner, end-to-end, open, and industry-level data governance solutions became the top priority. Although numerous AI-driven governance technologies are available, they are mostly purpose-built for generic corporate requirements and do not entirely meet the decision-making-oriented, ethics-conscious, and regulation-compliant insurance industry requirements. This paper presents a comparative evaluation of six top governance platforms—Collibra, Informatica CLAIRE, BigID, Immuta, IBM Watson Knowledge Catalog, and Alation—on eight dimensions, such as explainability, consent management, and insurance-specific flexibility. The research also illustrates the industry specific adoption of AI driven data governance in finance, health care and insurance along with a comparative insights amongst the three most data centric industry.  The study reviews insurance governance practices to assess capability gaps in the existing available commercial tools and strategic recommendations to insurers and tech vendors. This paper provides the basis for building AI governance systems that are compatible, scalable, fair, transparent, and flexible to the specific working context of the insurance data universe by overcoming technical limitations and moral dilemmas.

Large Language Models (LLMs) have achieved very good success in natural language processing, but deployment of these powerful models on edge computing devices across all domains presents unique challenges. This paper reviews the state of LLMs in edge computing, focusing on four key aspects: their emerging applications across various sectors, the technical challenges of running LLMs on resource-constrained edge devices, the potential benefits of bringing LLM capabilities closer to data sources, and effective deployment strategies to enable LLMs at the edge. We also discuss on how LLM edge deployment could offer low-latency, privacy-preserving intelligent assistance throughout a range of domains, such as healthcare, IoT, industrial automation, and more.  We also look at some techniques and architectures that can overcome the limitations of edge devices, such as cloud-edge collaboration, federated learning, model compression, and on-device inference. This review identifies practical ways to integrate LLMs into edge environments by examining current practices and their trade-offs. It also provides guidance for future research to address the remaining issues in this quickly expanding field.

Ensuring that financial technology solutions are effective, safe, and comply with regulations is necessary in the changing technology field. The research introduces a new Quality Assurance framework that ensures that FinTech systems follow strict rules, process transactions instantly, and have the most secure possible systems. Using up-to-date automated testing, optimization techniques, and CI/CD practices, the approach boosts the system’s reliability, scalability, and quick response. Research shows that using this approach boosts defect detection results, speeds up development, and reduces risks, setting a new high standard for QA in FinTech. This study provides useful information for both experts and academics working on improving software quality and system dependability in high-stakes finance.

Thanks to the swift development of AI and cloud computing, the healthcare industry is experiencing major changes. Before, traditional CRM only kept simple data and helped retrieve it. Still, thanks to AI and cloud solutions, they offer more organized, tailored, and patient-oriented care compared to before. The article focuses on using AI-based cloud CRM systems in healthcare to support better patient results and more efficient day-to-day activities. Using AI in CRM platforms, companies can spot upcoming needs for patients, assist doctors in making quick decisions, and streamline many routine jobs. Intelligent chatbots are used for patient interaction, patient sentiments are analyzed, and AI is used to manage how care should be delivered based on risk levels. With cloud infrastructure, healthcare can offer flexible storage, teamwork between departments, and remote access to its services. In addition, using blockchain for security, 5G, and edge computing allows instant access to information while caring for patients to ensure that health care is continuously active. Using these technologies with CRM systems, healthcare providers can improve their relationships with clients, reduce costs, and handle the growing challenges in healthcare. Current approaches and potential use of AI and cloud services for CRMs in healthcare are thoroughly discussed and analyzed in this paper.

Sales territory planning is crucial for maximizing sales performance, distributing workload evenly among representatives, and minimizing travel expenses. Manual assignments, rule-based systems, and simple clustering algorithms alike often fall short in terms of scalability, fairness, and adaptability to market dynamics. In this paper, a comprehensive graph-based framework is introduced that treats customers, depots, and travel paths as nodes and edges of a graph structure. The model incorporates multiple key attributes, including customer value, travel distance, and sales representative capacity, to generate geographically coherent, workload-balanced territories. These territories are also aligned with strategic business objectives. The framework supports dynamic adjustments, leveraging advanced feature engineering and preprocessing techniques to adapt to changing sales data and operational conditions. Experimental evaluations demonstrate that graph-based territory planning outperforms traditional approaches in terms of workload equity, the number of unused trips, and overall customer coverage. Additionally, the model's outputs are transparent and interpretable, enabling sales managers to make more informed and confident decisions. Looking forward, the use of real-time data sources, such as live traffic updates and customer activity logs, combined with machine learning approaches, presents an opportunity to enhance responsiveness and territory optimization further. This graph-based approach can also be applied in other domains beyond sales, such as service delivery, field maintenance, and healthcare outreach. The proposed framework offers a practical, scalable, and adaptable solution for modern sales organizations seeking to remain competitive in a complex and highly data-driven environment.

In recent years, a significant number of manufacturing enterprises globally have adopted Enterprise Resource Planning (ERP) systems as a strategic step toward digital transformation, leveraging advancements in cloud-based technologies. ERP systems, characterized by their comprehensive database structures, support advanced capabilities such as Artificial Intelligence (AI), Big Data analytics, Machine Learning (ML), and process automation. Given their integrative potential, these systems effectively consolidate essential business functions, including Sales, Accounting, Manufacturing, Human Resources, and overall management.

Data quality emerges as a critical factor and one of the foundational pillars for the successful implementation of ERP systems. The relevance of high-quality data in ERP deployments is underscored by its direct influence on operational efficiency, departmental integration, and informed decision-making at executive levels. Poor data quality during ERP implementation can result in significant adverse effects, disrupting interdepartmental coordination, and leading to flawed strategic decisions.

This review addresses key data quality issues commonly encountered during the data migration phase, transitioning from legacy systems to modern ERP infrastructures. It highlights prominent data quality challenges, including data inconsistencies, duplication, incompleteness, and misalignment across disparate data sources. Additionally, the paper explores various methodologies and best practices for enhancing data quality, such as rigorous data cleansing, robust governance frameworks, and systematic validation procedures during migration.

Furthermore, this study emphasizes the criticality of maintaining data integrity throughout ERP implementation phases and identifies effective ERP project management practices as vital to ensuring successful system deployment. Insights drawn from recent literature and empirical case studies illustrate the strategies employed to mitigate data quality risks, ensuring the realization of anticipated ERP system benefits.

The insurance industry is also affected by insurance fraud, which incurs massive financial losses and operational inefficiencies. Current fraud detection methods tend to be based on rule-based systems and static Extract, Transform, Load (ETL) pipelines, which are unable to keep up with the pace of rapidly evolving fraud tactics. However, these conventional approaches exhibit high false-positive rates, limited flexibility, and cannot perform real-time analysis, causing delayed detection and increased operational costs. This article describes the integration of machine learning (ML) techniques into Extract, Transform, and Load (ETL) pipelines to facilitate real-time, data-driven fraud identification during insurance claims processing. This system features embedded supervised machine learning classifiers within the ETL workflow, enabling dynamic analysis of claims data during ingestion and transformation. Temporal behavior modelling, behavior modelling, and external data source enrichment, co-enabled with fraud auto-registry, will allow the system to improve the detection of complex behaviors over time. Scalability and near real-time processing are supported by the pipeline orchestration, resulting in timely fraud risk scoring. The results of experiments demonstrate that the proposed methods yield a significant improvement in detection accuracy and latency reduction compared to traditional methods. By incorporating dimensionality reduction techniques, further optimization of model performance can be achieved. With this approach, claims processing can effectively evolve in lockstep with dynamic and ever-changing scales, adapting without impacting efficiency and resiliency. Ultimately, an ML-augmented ETL pipeline is proposed, which provides insurers with a powerful tool for reducing fraud losses while maintaining agility and compliance.