International Journal of Advanced Research in Computer Science & Technology(IJARCST)
Intelligent AI and Machine Learning–Based Financial Analytics for Secure SAP Systems with Real-Time Cloud Monitoring
In modern enterprises, financial data analytics and SAP system security are increasingly critical for operational efficiency and risk management. This paper presents an AI and machine learning-powered financial data analytics framework that integrates real-time cloud monitoring and predictive risk analytics to enhance SAP system security. The framework consolidates financial and operational data from SAP systems into cloud platforms, enabling high-performance, real-time analysis. Machine learning models detect anomalies, forecast potential risks, and identify fraudulent or suspicious activities proactively. Real-time cloud monitoring ensures continuous visibility into system performance, user activity, and security events, while predictive risk analytics enable preemptive mitigation of threats. By combining AI-driven insights with automated security controls and compliance enforcement, the framework strengthens SAP system security, supports informed decision-making, and optimizes financial operations in dynamic cloud environments.
AI-Enabled Cloud Architecture for Healthcare Real-Time Analytics and Cybersecurity in Financial Systems
The increasing digitization of healthcare and financial services has significantly enhanced operational efficiency but has also introduced heightened cybersecurity risks and challenges in managing real-time data. This paper proposes an AI-enabled cloud architecture designed to support healthcare real-time analytics while ensuring robust cybersecurity for financial systems. The proposed framework integrates advanced machine learning models with cloud-native principles, leveraging scalable APIs, microservices, and secure data pipelines to process and analyze high-volume, heterogeneous datasets in real time. AI-driven anomaly detection and predictive analytics enhance the system’s ability to identify cyber threats, detect fraud, and support informed decision-making in both healthcare and financial domains. Security-by-design principles, including encryption, access control, and continuous monitoring, are embedded to ensure compliance with regulatory standards such as HIPAA and financial industry requirements. Experimental evaluation demonstrates that the architecture achieves low-latency processing, high throughput, and accurate threat detection, outperforming traditional batch-based and non-API systems. The findings indicate that AI-enabled cloud architectures provide a scalable, secure, and efficient platform for integrating real-time analytics and cybersecurity across healthcare and financial infrastructures.
Integrating Large Language Models with Cloud-Based Digital Marketing Analytics for Secure Targeted Advertising in Healthcare ERP Web Applications
The integration of Large Language Models (LLMs) with cloud-based digital marketing analytics is revolutionizing targeted advertising within healthcare ERP (Enterprise Resource Planning) web applications. This combination allows for enhanced personalization, enabling healthcare organizations to deliver more relevant and effective marketing content to patients while maintaining strict data security standards. LLMs leverage patient data, such as demographics and medical history, to generate tailored advertising campaigns, improving engagement and outcomes. By utilizing cloud platforms, these systems can scale effortlessly, providing real-time analytics and ensuring compliance with healthcare regulations, such as HIPAA. The use of LLMs also enhances the efficiency and effectiveness of advertising strategies, ensuring improved ROI. This paper explores how this integration can create a secure, data-driven, and scalable solution for digital marketing in the healthcare sector.
AI-Powered Financial Federated Learning Architecture for Healthcare Cybersecurity on AWS Cloud
The growing adoption of cloud computing in healthcare and financial sectors has significantly enhanced operational efficiency and analytics-driven decision-making. However, it also exposes sensitive data to evolving cybersecurity threats, including data breaches, insider attacks, and fraud. This paper proposes an AI-Powered Financial Federated Learning Architecture for Healthcare Cybersecurity on AWS Cloud, a framework designed to provide secure, scalable, and intelligent protection for sensitive data. The framework leverages federated learning to enable collaborative AI model training across distributed financial and healthcare datasets without sharing raw data, ensuring privacy preservation. It integrates cloud-native services on AWS to support real-time threat detection, anomaly identification, and proactive cyber risk mitigation. Security mechanisms, including encryption, access control, and compliance monitoring, ensure alignment with regulatory standards such as HIPAA, PCI-DSS, and GDPR. Experimental evaluation demonstrates enhanced detection accuracy, reduced response latency, and robust protection against emerging cyber threats. This architecture provides a secure and intelligent solution for managing cybersecurity challenges in multi-institutional cloud environments.
An Intelligent AI–Cloud Machine Learning Framework for Cybersecurity in SAP Financial and Healthcare Systems
The increasing reliance on SAP-based cloud platforms in financial and healthcare organizations has accelerated digital transformation while simultaneously expanding the cyber attack surface. Sensitive financial transactions and electronic health records processed through SAP environments are frequent targets of cyber threats such as fraud, ransomware, insider attacks, and advanced persistent threats. Conventional security mechanisms are often insufficient to address the scale, complexity, and dynamic nature of these risks. To overcome these challenges, this paper proposes a Secure AI–Cloud Machine Learning Framework for Financial and Healthcare Cybersecurity in SAP Environments. The proposed framework integrates cloud-native AI and machine learning capabilities with SAP platforms, including SAP S/4HANA and SAP Business Technology Platform (BTP), to enable intelligent threat detection, risk analysis, and automated response. Machine learning models analyze SAP application logs, transactional data, user behavior, and network telemetry to identify anomalies and malicious activities in real time. The framework adopts a zero-trust security model, incorporates threat intelligence feeds, and supports continuous compliance with regulatory standards such as PCI-DSS, HIPAA, SOX, and GDPR. Experimental evaluation and SAP-centric use cases demonstrate improved detection accuracy, reduced incident response time, and enhanced visibility into cybersecurity risks across hybrid and multi-cloud SAP landscapes. The proposed approach offers a scalable, secure, and intelligent solution for strengthening cybersecurity resilience in financial and healthcare SAP ecosystems.
An End-to-End Cloud AI Framework for Petabyte-Scale Multi-Tenant Data: Azure DevOps–Integrated Fraud Detection and Risk Analytics
Enterprises operating in multi-tenant cloud environments face increasing challenges in detecting fraud and managing risk across petabyte-scale datasets. Traditional approaches often lack the scalability, automation, and intelligence required to handle dynamic, high-volume data from diverse sources. This paper proposes an end-to-end Cloud AI framework that integrates Azure DevOps for continuous deployment, monitoring, and management, enabling real-time fraud detection and risk analytics across multi-tenant systems. The framework leverages artificial intelligence and machine learning techniques to analyze transactional, behavioral, and contextual data at scale. Its architecture ensures secure tenant isolation, high availability, and efficient processing of massive datasets. By combining AI-driven analytics with cloud-native tools, the system provides predictive risk scoring, anomaly detection, and actionable insights for proactive fraud prevention. Experimental evaluation demonstrates improved detection accuracy, reduced false positives, and enhanced operational efficiency, highlighting the effectiveness of integrating AI with cloud DevOps practices for enterprise-scale fraud and risk management.
Enterprise Fraud Prevention with Causal Trace Miner–Enhanced Deep Learning, Cloud-Native DevSecOps Pipelines, and SAP HANA ERP Security Analytics
Enterprise fraud is escalating in complexity due to the rise of cloud-native computing, interconnected ERP systems, multi-channel digital transactions, and the rapid scaling of identity-driven workflows. Conventional fraud detection approaches relying on deterministic rules and isolated analytics are insufficient to identify hidden, multi-stage behaviors that span transactional, identity, cloud, and ERP environments. This research presents an integrated enterprise fraud prevention architecture that leverages Causal Trace Miner analytics, deep neural networks, SAP HANA ERP security analytics, and cloud-native DevSecOps pipelines. The proposed model reconstructs cross-system causality to expose fraudulent behavioral paths, while deep learning models—including LSTM, CNN-LSTM, and Autoencoders—enhance detection accuracy by capturing temporal and latent anomaly patterns within large-scale datasets. SAP HANA’s in-memory platform supports real-time processing of ERP workflows, user roles, and financial transactions. Cloud-native DevSecOps pipelines automate security scanning, CI/CD deployments, policy enforcement, and continuous monitoring of AI models to ensure resilience and adaptability against evolving cyber-fraud threats. Experimental evaluation indicates significant improvements in anomaly detection accuracy, reduced false positives, improved causal interpretability, and enhanced system resilience. The proposed framework delivers a comprehensive, scalable, and explainable enterprise fraud defense system suitable for modern financial, ERP, and cloud environments.
AI-Enabled SAP Cloud Architecture for Real-Time Fraud Detection and Cyber Attack Defense in Finance and Healthcare
The rapid digital transformation of financial and healthcare sectors has led to massive volumes of sensitive data being processed across cloud platforms, increasing exposure to fraud and cyber threats. This paper proposes an AI-driven intelligent SAP cloud architecture designed to enhance fraud detection and cyber defense in finance and healthcare environments. The proposed framework integrates SAP Business Technology Platform (BTP) with advanced artificial intelligence techniques, including machine learning and deep learning models, to enable real-time anomaly detection, predictive threat analysis, and automated response mechanisms. By leveraging cloud-native data services, secure data pipelines, and scalable analytics, the architecture supports high-throughput processing of structured and unstructured data while ensuring data privacy, compliance, and system resilience. The solution demonstrates how AI-enabled intelligence within SAP cloud ecosystems can improve detection accuracy, reduce false positives, and strengthen cyber defense capabilities across heterogeneous data sources. This work highlights the potential of intelligent SAP cloud architectures to provide robust, scalable, and secure solutions for safeguarding critical financial and healthcare information systems.
AI-Driven Cybersecurity Software for Fraud and Network Intrusion Detection in Financial Big Data Systems
The rapid growth of digital banking and financial trading systems has significantly increased the risk of cyber threats, including network intrusions and fraudulent activities. Traditional security mechanisms often struggle to handle the high volume, velocity, and variety of financial big data, leading to delayed threat detection and financial losses. This research proposes an AI-driven cybersecurity software framework that leverages machine learning algorithms, big data analytics, and real-time monitoring to detect and prevent fraudulent transactions and network intrusions. The proposed system integrates anomaly detection, predictive analytics, and automated threat response to enhance security, reduce false positives, and improve operational efficiency. By combining scalable big data processing with intelligent machine learning models, the framework ensures proactive and adaptive cybersecurity for banking and financial trading platforms. Experimental results on real-world financial datasets demonstrate high accuracy, robustness, and efficiency, highlighting the potential of AI-powered solutions in safeguarding critical financial infrastructures.
Transforming Hazardous Environment Compliance Through AI-Driven Transparency and Automation
The paper explains how intelligent compliance systems will help establish trust in systems of high-risk contexts or circumstances. The intelligent compliance system is enabled by innovative technologies which may include AI, IOT, and cloud, which enable firms to create trust through their capabilities of real-time monitoring, automated auditing, predictive analysis, and other technologies that aim to meet expectations of transparency, accountability, and reliability. This paper demonstrated how intelligent compliance systems will help foster reduced human error, improved decision making and ultimately increase stakeholder confidence by way of examples of practice and technology. At the same time, legitimate adoption or effectiveness of these systems will be related to the extent to which AI is interpretable, the ability to share responsibility, and the complexity of the engagement design that invites the user account for the final decision. In addition, and also because of the more complex legal framework, it will also be important to have ethical concerns considered, privacy-by-design issues, and responsible AI frameworks as trust is explored. Intelligent compliance will be a strategic enabler for safer, better, and higher trust operation in these high risk contexts or circumstances.
Innovating Safety and Trust: The Future of Intelligent Compliance in Hazardous Environments
Prioritizing safety and earning trust in high-risk work environments are critical components to successful operations. Many traditional safety programs emphasize only the retrospective review of reported incidents which not only hampers turnaround times, Nia and accuracy in reporting. This paper examined how AI-enabled compliance technologies are new and innovative ways of improving safety management through continuous compliance frameworks. These systems leverage AI, (Internet of Things - IOT devices (the sensors and cameras embedded within), and advanced monitoring protocols to provide real-time hazard detection, automatic regulatory compliance, and informational reporting. By sharing the responsibility for compliance through colleagues and other associates along with the proactive tracking of risk factors, these intelligent compliance solutions provide a higher degree of transparency, reduce unsafe events and legally provided organization compliance documentation. This form of technology can facilitate sustainable morale, productivity, and operational resilience. When properly implemented in audits, inspections, investigation, and accountability functions, organizations can simultaneously provide a much higher degree of employee and public safety. This occurs when hazards are assessed in a systematic way, along with a commitment to automation and data management, monitor training effectiveness, and a dedicated focus to continuous improvement and open reporting. An integrated intelligent compliance strategy will demonstrate a commitment to protect people and operations while supporting ongoing safety performance in high-risk industries.
Reimagining Commercial Insurance with AI: Intelligent Risk Assessment, Dynamic Pricing, and Predictive Claims Management
The commercial insurance industry is at a critical inflection point as traditional underwriting, pricing, and claims management models struggle to keep pace with the complexity, speed, and volatility of modern risk environments. Rapid advances in Artificial Intelligence (AI), machine learning, cloud computing, and Internet of Things (IoT) technologies are fundamentally reshaping how insurers assess risk, price policies, and manage claims across large-scale commercial portfolios. This paper presents a comprehensive technical exploration of how AI-driven architectures enable intelligent risk assessment, dynamic pricing, and predictive claims management in commercial insurance ecosystems. It examines end-to-end AI pipelines spanning real-time data ingestion, feature engineering, model training, explainable AI (XAI), and regulatory-compliant deployment. The study further integrates real-world industry case studies, including Zurich’s IoT-enabled risk scoring, Progressive’s telematics-driven pricing, and Lemonade’s AI-based claims automation. Regulatory alignment with the NAIC AI Governance Framework, the NIST AI Risk Management Framework, and the EU AI Act is also addressed. The paper demonstrates that AI-powered insurance platforms can significantly enhance underwriting precision, reduce claims settlement time, improve fraud detection, and enable continuous, behavior-driven premium optimization, positioning AI as the foundational engine of next-generation commercial insurance.
ETL-Driven Data Integration for Enhanced Pharmaceutical Manufacturer Rebate Processing
Pharmaceutical rebates are monetary incentives that are paid to payers (even health plan and pharmacy benefit managers) by a drug manufacturer in an effort to reduce drug cost and enhance availability in the marketplace. Rebates can be based on performance, volume, or other elements related to the drug manufacturers' pharmaceutical data about prescribers and practices, and span the range of levels of prescribing, formulary placement, and total healthcare spending. The eRebate platform allows pharmaceutical manufacturers and their wholesale distributors to do an automated management of rebates by providing visibility to rebate contracts, by checking and monitoring the information accurately, and payment is processed. Overall, business operations are more efficient and there is enhanced transparency. This is all accomplished through rigorous ETL data pipelines that gather, transform, and centralize drug manufacturer and payer data using software tools such as Informatica PowerCenter and SnapLogic. The eRebate methodology involves making iterative changes to be able to practice agile governance and data quality. As automated workflows and enhanced troubleshooting processes are established, manufacturers and their trading partners will have the means to successfully manage burdening processing of rebate data in order to issue refunds and conduct analysis that are useful for data-driven decisions to effectively administer rebates.
A Next-Generation Service Architecture for Dependable Rewards Processing
Cloud-based solutions utilize services or infrastructure hosted in the internet cloud that provide a cost-effective, scalable, and available computing resource to support agility and innovation by removing the on-premise hardware requirement and supporting the dynamic allocation of resources. The incentive platform enables its utilization of a scalable suite of reliable services, such as enhanced telemetry, serverless computing, container orchestration, and the management of secure APIs. This enables the incentive platform to meet high healthcare standards and simultaneously optimize excellence and user experience. The incentives platform is designed to provide multi-tenant platform capabilities of a cloud-native, scalable, highly configurable, and robust solution that modernizes the incentives rewards program management process and to support over 100% uptime, rapid onboarding process for clients, and better operational costs by bundling many legacy systems into a single application and service available to millions of users. The design, using metadata, facilites the swift implementation of customized individual incentives reward programs, with no impact or limitations on the originating platform, with the rich full connectivity to analytics, financial and CRM systems, which also enhances and supports decision making and transparency. The disruptive incentives platform will continue to progressive, to enable more delivery components, such AI led engagement analytics capabilities, improved interoperability, and continued automation of complex workflows, as positioned to be the foundational enabler of patient centered, value-driven, incentives based programs, aligned to personalized healthcare and the digitally enabled changing landscape of healthcare.
Comprehensive Audit Data Pipeline Architecture - Strategies for Modern Banking Audit, Compliance and Risk Management
The Institution Bank, which has over $2 trillion in assets, is one of the largest Bank & Financial Services Organizations in the world, classified as one of the largest Global Systemically Important Banks (GSIBs). Strong auditing capabilities are critical for managing an organization's risks and ensuring compliance with regulations and operating effectively. In this session, we will describe a holistic audit data pipeline that combines Audit Data Pipeline Architecture, Audit Data Analytics (using Python and Alteryx), and the overall direction of the IIA standards as specified by the Institute of Internal Auditors. The architecture incorporates machine learning for predictive analytics, multi-source data intake, and transformation through ETL and the identification of the conflicts of interest. Performance metrics metrics such as throughput, latency, error rates, and data quality are considered to improve the performance of the pipeline. Visualizations and example datasets demonstrate effective audit monitoring workflows. The structured Audit Data Pipeline Architecture provides the audit function a comprehensive structure to find control deficiencies and meet continuous monitoring expectations within banking environments while meeting regulatory expectations of G-SIBs. Recommendations for future improvement of the pipeline to improve controls amidst the changing financial landscape, include real-time streaming, advanced anomaly detection techniques, explainable AI Models, and blockchain-based audit trails.
Leveraging Integrated Master Data and Claims Pipelines to Transform Medication Synchronization in Pharmacy Services
Rx Sync is a data-focused initiative designed to enhance pharmacy services with improved patient graduation from a medication-taking behavior using data aggregation and analytics; it draws data from many sources, compiling it into a discrete, more effective Product Data Store (PDS). The end product is trustworthy patient profiles complete with layers of data management processes to ensure consistent accuracy of data and linkage of data into actual profiles, and data management processes. The work has resulted in a historic reduction of short-acting opioid prescriptions by 31% and since then has also realized operational efficiencies for improved refill bundling, substitution generics, and home delivery possibilities with potential implications for cost and health outcomes. Utilizing scalable ETL standards and distributed computing technologies, Rx Sync was also able to sustain engagement to address challenges from data silos and data types/processes of designated larger datasets and to still be respectful of patient preferences. The timing of continuous update and feedback loops has also enhanced Rx Sync's performance and value as a project showcasing how the healthcare sector continues to need to transition and embrace Big Data strategies for ultimately higher value patient-centered care and pharmacy operations improvement overall.
A Secure Micro-Frontend and Cloud Data Orchestration Architecture for Enterprise Web Platforms
Enterprise web platforms, facing increasing demands for development agility and stringent data security compliance (e.g., GDPR, HIPAA), are constrained by the architectural misalignment between monolithic frontends and distributed microservices backends. While Micro-Frontends (MFEs) solve frontend scalability, they introduce complex security challenges, particularly around granular data access and cross-MFE communication. This paper proposes the Secure Micro-Frontend and Cloud Data Orchestration Architecture (SMF-CDOA), an integrated framework that addresses this security-agility trade-off. SMF-CDOA centralizes data access governance via a Secure Data Orchestrator (SDO), which acts as the sole ingress point for all backend services, enforcing Zero-Trust (ZT) policies based on MFE identity and user context. Furthermore, MFEs are composed at runtime using a Policy-Aware Shell (PAS) that manages inter-MFE data sharing via a controlled event bus, minimizing global state risk. The empirical evaluation demonstrates that SMF-CDOA achieves a $\mathbf{45\%}$ reduction in the complexity of managing data access policies by centralizing policy enforcement at the SDO. Crucially, the architecture maintained $\mathbf{100\%}$ isolation against simulated cross-MFE data leakage scenarios, establishing a resilient and scalable blueprint for securing decomposed enterprise web platforms.
Smart Machines, Smarter Outcomes the Rise of Self-Learning Systems
The evolution of smart machines into fully autonomous machine learning systems that are able to selflearn to improve performance. These systems use state-of-the-art artificial intelligence approaches, such as reinforcement learning, meta-learning, or sensor fusion. These ecosystems create unique opportunities to drive transformational impact across industries through optimally enhancing resilience, efficiency and intelligent decisionmaking capability. As per the findings, it is evident that we need an ecosystem-wide approach to design and enact frameworks that encompass authentic human systems-AI collaborations and governance mechanisms that are ethical, this will minimise risk around emerging technologies and maximise society's potential benefits. In summary, selflearning AI is shaping up to be one of the tent pole elements of the next technological and industrial revolution that can lead to better and more intelligent, safer, and sustainable world-wide outcomes for humanity. As indicated in the research, we will need to take a proactive, multidisciplinary approach to leveraging the evolving nature of self-learning AI systems so we can know the implications and prepare our economic and social systems accordingly.
The Evolving Landscape of Hardware and Firmware Engineering in Cloud Infrastructure
This study presents the idea of integrating hardware and firmware engineering in the context of company cloud-based architectures to support performance, stability, and security for workloads involving AI, big data, and edge computing. The research points to co-development being essential through the presentation of co-developing techniques, illustrated by some examples, including Microsoft Azure Cobalt 100 CPU and Heritage Maia 100 AI Accelerator. The study contends that techniques involved in co-development lifecycles would support improving validation time and redundancy through automated regression testing, virtualized simulations, and agile processes. The co-development methodology involves hardware engineers, firmware developers, software architects, and cloud operators working effectively together as integrated teams to identify and fix both scaling issues and integrated aspects. There is mention of trends that respond to security and reliability challenges that are commonplace in large cloud data centers, including AI-based automated testing, validation-based digital twin, and continuous, regular firmware updates. Most importantly, the research highlights _co-developing_ firmware stacks for mixed heterogeneous computing (accelerators) engaged with DevOps management lifecycle as a clear goal. Ultimately, this research affirms that an integrated partnership of hardware/firmware is required to deliver available, reliable, scalable, and, importantly, secure cloud services to improve customer experience while taking advantage of the rapid nature of digital business transformation in the cloud.
Harnessing Artificial Intelligence for Salesforce Metadata Advanced Migration Strategies and Strategic Business Benefits
Artificial intelligence is an expanding speciality of computer science concentrated on developing stateof-the-art structures and algorithms to automate complex tasks. Salesforce configurations are becoming more and more complex; therefore, the demand for a rapid and accurate impact analysis increases. This supports the contention that manual methods for identifying changes cannot provide the advantages mentioned earlier since the manual impact analysis takes time and is prone to manual errors. Artificial intelligence can be applied to automate the process of identifying changed data in Salesforce. Thus, the use of artificial intelligence can increase the capability for quicker, accurate impact analyses while decreasing the amount of manual effort typically needed for both ease of audits and compliance. Artificial intelligence also allows for analysis of vast historical datasets, enabling a more accurate identification of changes while reducing false-positives. A significant advantage of using artificial intelligence is enhanced optimization of regression testing using intelligent dependency mapping, leading to consistent impact analyses using artificial intelligence, while enabling a quality check of the impact analyses with domain expertise. Quicker releases with lower operational costs can result. Artificial intelligence also provides robust solutions that are always learning based on the input of the user, although for organizational operational to be met, data intensive enterprise-specific models need often to be built. Consequently, AI systems related to Salesforce configurations are advancing more and more to provide sophisticated and efficient impact analysis and automation capabilities.
CYPRESS PERFORMANCE INSIGHTS PREDICTING UI TEST EXECUTION TIME USING COMPLEXITY METRICS
This study presents a comparative analysis of three machine learning regression algorithms—Linear Regression (LR), Multi-layer Perceptron (MLP), and Gaussian Process Regression (GPR)—for predicting software execution time based on critical testrelated input parameters. The aim is to evaluate model performance in accurately estimating execution time using features such as Test Script Complexity, DOM Element Count, and Test Data Size (in KB). A robust evaluation was carried out using multiple metrics, including R² score, Explained Variance Score (EVS), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Log Error (MSLE), and others. The results indicate that MLP outperforms the other models on test Cypress Performance Insights: Predicting UI Test Execution Time Using Complexity Metrics https://iaeme.com/home/journal/IJRCAIT 168 editor@iaeme.com data, showcasing high accuracy and generalization capability, making it the most suitable model for predictive automation scenarios. Research Significance: With increasing demands for automation in software testing, accurately predicting test execution time has become crucial for optimizing resource allocation, managing testing pipelines, and improving CI/CD efficiency. Traditional heuristic-based estimations often fail to capture complex nonlinear dependencies between test attributes and execution performance. This research contributes to the field by applying and benchmarking advanced machine learning models to quantify these relationships, offering a data-driven framework for execution time prediction in test automation environments. Methodology: Algorithm Analysis The study utilizes three supervised learning regression algorithms: Linear Regression (LR): A baseline model to understand linear relationships. Multi-layer Perceptron (MLP): A neural network-based model capable of capturing nonlinear patterns. Gaussian Process Regression (GPR): A probabilistic model that estimates uncertainty along with predictions. The input dataset includes the following features: Test Script Complexity, DOM Element Count, Test Data Size (in KB). The target variable is: Execution Time (in seconds). Each model was trained on a structured dataset and evaluated using a comprehensive set of regression metrics on both training and test data. Alternative: Input Parameter The predictive model was built using the following input features that influence execution time: Test_Script_Complexity: Indicates the logical and structural difficulty of the test script. DOM_Element_Count: Number of Document Object Model elements present, representing UI complexity. Test_Data_Size_KB: Volume of input test data in kilobytes, which impacts processing time. Evaluation Parameter: Output The output parameter being predicted is: Execution_Time_Seconds: The total time (in seconds) taken to execute a test case/script from start to finish. Result: The evaluation results highlight significant differences in performance across the three models. MLP achieved the highest R² (≈ 0.98) and the lowest error metrics (MSE ≈ 0.019, RMSE ≈ 0.139) on the test data, indicating excellent generalization and predictive accuracy. GPR performed moderately, while Linear Regression, though simpler, lagged behind in accuracy and error consistency. The findings support the use of MLP as the most robust and reliable model for predicting execution time in dynamic test environments
An AI-Driven Cloud Security Framework for Fraud Detection in Banking and Financial Markets Using Machine Learning and Deep Learning
Financial fraud—ranging from credit-card theft and identity misuse to market manipulation—presents a persistent, evolving threat to banks, payment processors, and capital markets. This paper proposes an AI-driven cloud security framework that integrates scalable data ingestion, feature engineering, ensemble machine learning (ML) classifiers, and deep learning (DL) architectures with real-time streaming and cloud native security controls to improve fraud detection accuracy and timeliness. The framework combines supervised (random forests, gradient boosting, and deep neural networks) and unsupervised/anomaly detection (autoencoders, clustering, isolation forests) techniques to address class imbalance and concept drift, and leverages explainability modules for regulatory transparency. A layered cloud security posture—identity and access management (IAM), encryption, logging/monitoring, and compliance checkpoints (PCI-DSS/Risk matrices)—is embedded to meet financial regulatory requirements. Experiments on benchmark and synthetic datasets show significant gains in detection F1 and reduced false positives when hybrid ML+DL ensembles and continuous model re-training are applied in a cloud streaming environment. We discuss operational tradeoffs, privacy-preserving approaches (differential privacy, federated learning), and deployment considerations for low-latency scoring. The framework aims to deliver an adaptable, auditable, and scalable solution for modern banking and market infrastructures.
Secure Healthcare Data Exchange and Financial Fraud Detection Using AI-Driven Cloud Data Integration
The rapid digitization of healthcare systems and the migration of sensitive clinical and financial data to cloud environments have significantly improved interoperability, scalability, and operational efficiency. However, this transformation has simultaneously increased exposure to cyber threats, data breaches, and financial fraud, including insurance fraud, billing manipulation, and unauthorized access to protected health information (PHI). Traditional security mechanisms and rule-based fraud detection systems are increasingly inadequate in addressing the scale, complexity, and evolving nature of these threats. This paper proposes an integrated framework for secure healthcare data exchange and financial fraud detection using artificial intelligence (AI)-driven cloud data integration. The framework combines machine learning-based fraud analytics, secure cloud architectures, encryption mechanisms, and network-level monitoring to enable real-time detection and prevention of anomalous activities. We examine how AI models leverage heterogeneous healthcare data sources—such as electronic health records (EHRs), insurance claims, payment transactions, and access logs—to identify fraudulent patterns while maintaining compliance with data protection regulations. A comprehensive methodology covering data ingestion, feature engineering, model training, explainability, and deployment is presented. Experimental results and literature-backed evaluations demonstrate that AI-driven approaches significantly outperform traditional methods in detection accuracy and response time. The study concludes with insights into current limitations and outlines future research directions, including federated learning and privacy-preserving analytics for secure healthcare ecosystems.
Next-Generation AI and Cloud Architecture for Scalable Fraud Detection in SAP HANA
The exponential growth of digital banking and cloud-native financial services has intensified the scale, velocity, and complexity of fraudulent activities, demanding intelligent and highly scalable detection mechanisms. Traditional fraud detection systems struggle to process high-volume, real-time transactions while maintaining accuracy and regulatory compliance. This paper presents a next-generation AI and cloud architecture for scalable fraud detection in SAP HANA–based banking systems. The proposed framework leverages SAP HANA Cloud’s in-memory computing capabilities combined with AI-driven machine learning and deep learning models to enable low-latency, real-time fraud analytics. Cloud-native microservices and DevSecOps pipelines support continuous model training, deployment, and monitoring, ensuring adaptability to evolving fraud patterns. The architecture integrates transactional analytics, behavioral profiling, and network security signals to enhance fraud intelligence and reduce false positives. Built-in security and governance mechanisms align with financial regulatory requirements while supporting elastic scalability and high availability. Experimental results indicate that the proposed AI-cloud framework significantly improves fraud detection accuracy, response time, and operational resilience compared to conventional on-premise and rule-based approaches. The study demonstrates how SAP HANA Cloud can serve as a robust foundation for next-generation, intelligent fraud detection in modern banking ecosystems.
A DevOps-Enabled AI Analytics Pipeline for Fraud Detection and Cyber Risk Mitigation in SAP HANA Cloud
The rapid digitalization of banking and enterprise systems has significantly increased exposure to financial fraud and cyber risks. Modern platforms such as SAP HANA Cloud process massive volumes of real-time transactional and operational data, making them attractive targets for sophisticated cyber-attacks and fraud schemes. This paper proposes a DevOps-enabled AI analytics pipeline for fraud detection and cyber risk mitigation deployed on SAP HANA Cloud. The framework integrates machine learning (ML) and deep learning (DL) models with continuous integration and continuous deployment (CI/CD), real-time analytics, and automated security controls. By embedding AI-driven fraud detection within a DevSecOps lifecycle, the proposed pipeline enables rapid model iteration, real-time threat intelligence ingestion, and proactive cyber defense. The architecture leverages SAP HANA’s in-memory computing, cloud-native services, and containerized MLOps workflows to ensure scalability, resilience, and compliance. Experimental analysis demonstrates improved fraud detection accuracy, reduced detection latency, and enhanced cyber risk visibility compared to traditional rule-based systems. The study highlights how DevOps principles enhance AI governance, model reliability, and security posture in enterprise cloud environments. The findings contribute to both academic research and industry practice by presenting a scalable, secure, and continuously adaptive framework for fraud and cyber risk management in SAP HANA Cloud ecosystems.
Real-Time Fraud Detection and Cybersecurity in SAP HANA Using Machine Learning, Deep Learning, and DevSecOps Automation
This paper presents an integrated approach for real-time fraud detection and cybersecurity in enterprise SAP HANA systems by combining machine learning (ML), deep learning (DL), and DevSecOps automation. Modern financial, retail, and enterprise ecosystems demand sub-second detection of anomalous transactions while maintaining system integrity, compliance, and traceability. SAP HANA’s in-memory processing and embedded analytics (PAL/APL) provide a low-latency platform suitable for operationalizing ML models in production. We propose a layered architecture: (1) real-time ingestion and feature engineering using change data capture (CDC) and streaming pipelines, (2) hybrid detection models — ensembles of gradient-boosted decision trees and DL sequence/graph models for transactional and relational anomalies, (3) explainability and risk scoring layers to reduce false positives, and (4) DevSecOps pipelines for automated model testing, secure model deployment, continuous monitoring, and incident response. Experimental and scenario-based results show that the hybrid approach reduces detection latency to near real-time and improves detection performance on imbalanced transactional datasets while DevSecOps automation reduces mean time to remediate vulnerabilities and model drift. We discuss advantages, limitations, and propose future work including graph neural networks for relational fraud and federated privacy-preserving training for cross-institution collaboration.
AI-Driven Cyber Fraud Detection in Financial and Healthcare Ecosystems Using Cloud Deep Learning
The rapid digital transformation of financial services and healthcare ecosystems has significantly increased exposure to sophisticated cyber fraud and security threats in cloud computing environments. Traditional rule-based and signature-driven security mechanisms are inadequate to detect complex, high-velocity fraud patterns across heterogeneous data sources. This paper proposes an AI-driven cyber fraud detection framework that leverages cloud-based deep learning architectures to provide scalable, real-time, and adaptive fraud intelligence for both financial and healthcare domains. The proposed model integrates transactional, network, and healthcare data streams using secure cloud data pipelines, enabling cross-domain correlation and anomaly detection. Deep learning techniques, including deep neural networks and recurrent architectures, are employed to learn non-linear fraud patterns and evolving attack behaviors, while network security analytics enhance threat contextualization and response accuracy. The framework supports cloud-native deployment with DevSecOps automation, ensuring continuous monitoring, rapid model updates, and regulatory-aware security controls suitable for financial compliance and healthcare data protection. Experimental evaluation demonstrates improved detection accuracy, reduced false positives, and enhanced resilience against advanced persistent threats compared to conventional machine learning approaches. The results highlight the effectiveness of AI-enabled cloud deep learning in delivering robust cyber fraud intelligence across interconnected financial and healthcare ecosystems.
Intelligent Fraud Detection in Cloud Computing Using AI-Enabled Machine Learning and Network Security Analytics
Cloud computing platforms host a vast array of services and large-scale transactions, making them attractive targets for a spectrum of fraudulent activities — from account takeover and payment fraud to lateral movement and exfiltration. Traditional rule-based defenses and siloed network controls struggle to keep up with adversaries who exploit scale, automation, and new service models. This paper examines intelligent fraud detection at the intersection of machine learning (ML) and network security within cloud environments. We survey threat surfaces unique to clouds, characterize the data sources available (logs, network flows, API calls, telemetry), and review ML approaches — supervised, unsupervised, semi-supervised, and deep learning — that are applicable to cloud fraud detection. We present an end-to-end research methodology for building cloud-aware fraud detection systems, including data collection and labeling, feature engineering at multiple abstraction levels, model selection, concept-drift handling, online evaluation, interpretability, and deployment considerations (scalability, privacy, cost). Advantages (enhanced detection, automated adaptation) and disadvantages (data imbalance, adversarial evasion, privacy concerns, operational cost) are discussed. Results from representative experiments and literature benchmarks show that hybrid architectures combining network-level anomaly detection with transaction-level classification produce the best trade-offs between precision and recall in cloud settings. We conclude with recommendations and a prioritized agenda for future work: federated learning for multi-tenant privacy, adversarially robust models, and standardized evaluation benchmarks for cloud fraud.
Cloud-Native AI and Deep Learning Models for Real-Time Fraud Detection and Cybersecurity in Financial Institutions
Financial institutions face escalating fraud and cyber-threat volumes as transaction velocity, digital channels, and adversary sophistication grow. Real-time detection is essential to minimize monetary loss and reputational damage, but it requires systems that combine low latency, high throughput, adaptive learning, privacy preservation, and operational resilience. This paper investigates cloud-native architectures and deep learning (DL) models tailored for real-time fraud detection and cybersecurity in banking, payments, and capital-market environments. We synthesize best practices from stream processing, model serving, feature stores, and MLOps to propose an end-to-end reference architecture: event ingest (Kafka/pub-sub) → streaming feature extraction (Flink / ksqlDB) → ensemble hybrid DL + graph models (LSTM/Transformer + GNN) with online learning, served via Kubernetes/Kubeflow for autoscaling, A/B canarying, monitoring, and explainability (SHAP/local explanations). The methodology covers data labeling strategies, concept-drift detection and mitigation, privacy techniques (differential privacy, federated learning), and regulatory alignment. In experiments (simulated and production-like replay), hybrid graph-aware DL models improve detection recall for organized/relational fraud patterns while keeping false positives within operational tolerance when combined with risk-based scoring and human analyst-in-the-loop triage. We discuss tradeoffs — latency, interpretability, model decay, and privacy — and provide recommendations for practitioners and researchers to deploy robust, auditable, cloud-native fraud detection systems.
Driving Salesforce Testing Excellence with AI and Metadata-Driven Intelligent Automation
Salesforce is the most well-known cloud application-based CRM solution, known for its scalability, analytics, and vast application ecosystem that allow companies to better understand their processes and relationships with customers. Despite the benefits, the CRM can be daunting and expensive for smaller teams, requiring a trained resource for upkeep and introducing challenges when integrating and managing other technologies. To overcome these limitations, the Robot Project is integrating Salesforce Smart Works 2.0. Smart Works 2.0 improves dependency mapping, detects changes, and does regression testing through a metadata-driven architecture. The Smart Work integration into Salesforce will streamline these initiatives by creating improved processes and enhancing testing coverage while also ensuring quality in released code for its users in highly regulated businesses (such as healthcare). Smart Works 2.0 also provides a comprehensive testing suite to accomplish these capabilities and ensures easily obtainable advanced automation features. The overall ROI and efficient delivery rates for continued developments in all automated and AI-driven testing and business release changes will include a metadata-centric DevOps pipeline, automated governance, and real-time analytics - connecting both robotic and AI agents in testing and development processes while assisting Autodesk development teams. In combination, the development of low-code or even no-code platforms will allow more business users to be able to maintain and complete intelligent testing initiatives for continuous innovation resources and process work through reduction or avoidance of traditional bottleneck development.