International Journal of Advanced Research in Computer Science & Technology(IJARCST)
type: channel
language: en
publication name: International Journal of Advanced Research in Computer Science & Technology(IJARCST)
items: Intelligent AI and Machine Learning–Based Financial Analytics for Secure SAP Systems with Real-Time Cloud Monitoring
items: AI-Enabled Cloud Architecture for Healthcare Real-Time Analytics and Cybersecurity in Financial Systems
items: Integrating Large Language Models with Cloud-Based Digital Marketing Analytics for Secure Targeted Advertising in Healthcare ERP Web Applications
items: AI-Powered Financial Federated Learning Architecture for Healthcare Cybersecurity on AWS Cloud
items: An Intelligent AI–Cloud Machine Learning Framework for Cybersecurity in SAP Financial and Healthcare Systems
items: An End-to-End Cloud AI Framework for Petabyte-Scale Multi-Tenant Data: Azure DevOps–Integrated Fraud Detection and Risk Analytics
items: Enterprise Fraud Prevention with Causal Trace Miner–Enhanced Deep Learning, Cloud-Native DevSecOps Pipelines, and SAP HANA ERP Security Analytics
items: AI-Enabled SAP Cloud Architecture for Real-Time Fraud Detection and Cyber Attack Defense in Finance and Healthcare
items: AI-Driven Cybersecurity Software for Fraud and Network Intrusion Detection in Financial Big Data Systems
items: Transforming Hazardous Environment Compliance Through AI-Driven Transparency and Automation
items: Innovating Safety and Trust: The Future of Intelligent Compliance in Hazardous Environments
items: Reimagining Commercial Insurance with AI: Intelligent Risk Assessment, Dynamic Pricing, and Predictive Claims Management
items: ETL-Driven Data Integration for Enhanced Pharmaceutical Manufacturer Rebate Processing
items: A Next-Generation Service Architecture for Dependable Rewards Processing
items: Comprehensive Audit Data Pipeline Architecture - Strategies for Modern Banking Audit, Compliance and Risk Management
items: Leveraging Integrated Master Data and Claims Pipelines to Transform Medication Synchronization in Pharmacy Services
items: A Secure Micro-Frontend and Cloud Data Orchestration Architecture for Enterprise Web Platforms
items: Smart Machines, Smarter Outcomes the Rise of Self-Learning Systems
items: The Evolving Landscape of Hardware and Firmware Engineering in Cloud Infrastructure
items: Harnessing Artificial Intelligence for Salesforce Metadata Advanced Migration Strategies and Strategic Business Benefits
items: CYPRESS PERFORMANCE INSIGHTS PREDICTING UI TEST EXECUTION TIME USING COMPLEXITY METRICS
items: An AI-Driven Cloud Security Framework for Fraud Detection in Banking and Financial Markets Using Machine Learning and Deep Learning
items: Secure Healthcare Data Exchange and Financial Fraud Detection Using AI-Driven Cloud Data Integration
items: Next-Generation AI and Cloud Architecture for Scalable Fraud Detection in SAP HANA
items: A DevOps-Enabled AI Analytics Pipeline for Fraud Detection and Cyber Risk Mitigation in SAP HANA Cloud
items: Real-Time Fraud Detection and Cybersecurity in SAP HANA Using Machine Learning, Deep Learning, and DevSecOps Automation
items: AI-Driven Cyber Fraud Detection in Financial and Healthcare Ecosystems Using Cloud Deep Learning
items: Intelligent Fraud Detection in Cloud Computing Using AI-Enabled Machine Learning and Network Security Analytics
items: Cloud-Native AI and Deep Learning Models for Real-Time Fraud Detection and Cybersecurity in Financial Institutions
items: Driving Salesforce Testing Excellence with AI and Metadata-Driven Intelligent Automation

Intelligent AI and Machine Learning–Based Financial Analytics for Secure SAP Systems with Real-Time Cloud Monitoring
type: item
description: 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.
subject: Articles
  topic: section
subject: AI-Powered Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Financial Data Analytics
  topic: keywords
subject: SAP System Security
  topic: keywords
subject: Real-Time Cloud Monitoring
  topic: keywords
subject: Predictive Risk Analytics
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Operational Efficiency
  topic: keywords
creator: S.Saravana Kumar
rights: Copyright (c) 2024
date: 2024-09-10
publication date: 2024-09-10
starting page: 10974
ending page: 10980
doi: 10.15662/IJARCST.2024.0705012

AI-Enabled Cloud Architecture for Healthcare Real-Time Analytics and Cybersecurity in Financial Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: AI-enabled cloud architecture
  topic: keywords
subject: Healthcare real-time analytics
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Financial systems
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Predictive analytics
  topic: keywords
creator: Hugo Alexei Dmitri
rights: Copyright (c) 2024
date: 2024-08-10
publication date: 2024-08-10
starting page: 10656
ending page: 10661
doi: 10.15662/IJARCST.2024.0704010

Integrating Large Language Models with Cloud-Based Digital Marketing Analytics for Secure Targeted Advertising in Healthcare ERP Web Applications
type: item
description: 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.
subject: Articles
  topic: section
subject: Large Language Models
  topic: keywords
subject: Cloud-based Analytics
  topic: keywords
subject: Digital Marketing
  topic: keywords
subject: Targeted Advertising
  topic: keywords
subject: Healthcare ERP
  topic: keywords
subject: Data Security
  topic: keywords
subject: Personalization
  topic: keywords
subject: HIPAA Compliance
  topic: keywords
subject: Healthcare Marketing
  topic: keywords
subject: Real-time Analytics
  topic: keywords
subject: Scalable Solutions
  topic: keywords
subject: Patient Data
  topic: keywords
creator: George Harrison Montgomery
rights: Copyright (c) 2022
date: 2022-05-20
publication date: 2022-05-20
starting page: 6594
ending page: 6602
doi: 10.15662/IJARCST.2022.0503004

AI-Powered Financial Federated Learning Architecture for Healthcare Cybersecurity on AWS Cloud
type: item
description: 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.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Financial Systems
  topic: keywords
subject: Healthcare Cybersecurity
  topic: keywords
subject: AWS Cloud
  topic: keywords
subject: Risk Management
  topic: keywords
subject: Data Privacy
  topic: keywords
creator: Kieran Michael Nolan
rights: Copyright (c) 2023
date: 2023-09-22
publication date: 2023-09-22
starting page: 9023
ending page: 9030
doi: 10.15662/IJARCST.2023.0605010

An Intelligent AI–Cloud Machine Learning Framework for Cybersecurity in SAP Financial and Healthcare Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: AI–Cloud Computing
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: SAP Security
  topic: keywords
subject: Cybersecurity Framework
  topic: keywords
subject: Financial Systems
  topic: keywords
subject: Healthcare Systems
  topic: keywords
subject: Risk Management
  topic: keywords
creator: John Samuel Prabakaran
rights: Copyright (c) 2024
date: 2024-05-14
publication date: 2024-05-14
starting page: 79
ending page: 86
doi: 10.15662/IJARCST.2024.0701812

An End-to-End Cloud AI Framework for Petabyte-Scale Multi-Tenant Data: Azure DevOps–Integrated Fraud Detection and Risk Analytics
type: item
description: 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.
subject: Articles
  topic: section
subject: Cloud AI
  topic: keywords
subject: Multi-Tenant Data
  topic: keywords
subject: Petabyte-Scale Analytics
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Risk Analytics
  topic: keywords
subject: Azure DevOps
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Enterprise Cloud Systems
  topic: keywords
subject: Scalable Cloud Framework
  topic: keywords
subject: Data Security
  topic: keywords
creator: Abdul Hakim Mohamed
rights: Copyright (c) 2024
date: 2024-05-22
publication date: 2024-05-22
starting page: 79
ending page: 85
doi: 10.15662/IJARCST.2024.0701811

Enterprise Fraud Prevention with Causal Trace Miner–Enhanced Deep Learning, Cloud-Native DevSecOps Pipelines, and SAP HANA ERP Security Analytics
type: item
description: 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.
subject: Articles
  topic: section
subject: Causal Trace Miner
  topic: keywords
subject: Enterprise Fraud Prevention
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: SAP HANA ERP
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: DevSecOps Pipelines
  topic: keywords
subject: Neural Networks
  topic: keywords
subject: Real-Time Analytics
  topic: keywords
subject: LSTM
  topic: keywords
subject: Autoencoders
  topic: keywords
subject: Process Mining
  topic: keywords
subject: Financial Security
  topic: keywords
subject: Identity Threat Detection
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: CI/CD Automation
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Transaction Monitoring
  topic: keywords
subject: ERP Intelligence
  topic: keywords
subject: Cloud-Native Security
  topic: keywords
subject: Event Causality
  topic: keywords
creator: Bruno Leonardo Fernandes
rights: Copyright (c) 2021
date: 2023-05-02
publication date: 2023-05-02
starting page: 4848
ending page: 4855
doi: 10.15662/IJARCST.2021.0403002

AI-Enabled SAP Cloud Architecture for Real-Time Fraud Detection and Cyber Attack Defense in Finance and Healthcare
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: SAP Cloud Architecture
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Cyber Defense
  topic: keywords
subject: Financial Systems
  topic: keywords
subject: Healthcare Analytics
  topic: keywords
subject: Cloud Security
  topic: keywords
creator: Rajesh Kumar K
rights: Copyright (c) 2024
date: 2024-12-27
publication date: 2024-12-27
starting page: 11409
ending page: 11416
doi: 10.15662/IJARCST.2024.0706028

AI-Driven Cybersecurity Software for Fraud and Network Intrusion Detection in Financial Big Data Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Big Data
  topic: keywords
subject: Network Intrusion Detection
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Financial Systems
  topic: keywords
subject: Banking Security
  topic: keywords
subject: Trading Systems
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Real-Time Monitoring
  topic: keywords
creator: Rajagopalachari Srinivasan Subramanian
rights: Copyright (c) 2025
date: 2025-12-25
publication date: 2025-12-25
starting page: 13218
ending page: 13224
doi: 10.15662/IJARCST.2025.0806019

Transforming Hazardous Environment Compliance Through AI-Driven Transparency and Automation
type: item
description: 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.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: IOT
  topic: keywords
subject: Intelligent compliance
  topic: keywords
subject: Human Error
  topic: keywords
subject: Transparency
  topic: keywords
subject: Accountability
  topic: keywords
creator: Sukruthi Reddy Sangannagari
rights: Copyright (c) 2025
date: 2025-09-10
publication date: 2025-09-10
starting page: 12933
ending page: 12939
doi: 10.15662/sc8kkt43

Innovating Safety and Trust: The Future of Intelligent Compliance in Hazardous Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Continuous Compliance
  topic: keywords
subject: Hazard Detection
  topic: keywords
subject: IoT
  topic: keywords
subject: Preemptive Risk Management
  topic: keywords
creator: Sukruthi Reddy Sangannagari
rights: Copyright (c) 2023
date: 2023-10-11
publication date: 2023-10-11
starting page: 9016
ending page: 9022
doi: 10.15662/at1t5775

Reimagining Commercial Insurance with AI: Intelligent Risk Assessment, Dynamic Pricing, and Predictive Claims Management
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Commercial Insurance
  topic: keywords
subject: Intelligent Underwriting
  topic: keywords
subject: Dynamic Pricing
  topic: keywords
subject: Predictive Claims Analytics
  topic: keywords
subject: Telematics
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Insurance Analytics
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
creator: Sukruthi Reddy Sangannagari
rights: Copyright (c) 2024
date: 2024-01-10
publication date: 2024-01-10
starting page: 9700
ending page: 9711
doi: 10.15662/wdj4rf44

ETL-Driven Data Integration for Enhanced Pharmaceutical Manufacturer Rebate Processing
type: item
description: 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.
subject: Articles
  topic: section
subject: Pharmaceutical rebates
  topic: keywords
subject: eRebate
  topic: keywords
subject: ETL data pipelines
  topic: keywords
subject: Informatica PowerCenter
  topic: keywords
subject: SnapLogic
  topic: keywords
creator: Venkat Sunil Kumar Indurthy
rights: Copyright (c) 2025
date: 2025-01-10
publication date: 2025-01-10
starting page: 11606
ending page: 11615
doi: 10.15662/9cfsb587

A Next-Generation Service Architecture for Dependable Rewards Processing
type: item
description: 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.
subject: Articles
  topic: section
subject: fostering agility
  topic: keywords
subject: container orchestration
  topic: keywords
subject: secure API management
  topic: keywords
subject: CRM systems
  topic: keywords
creator: Murugan Ambalakannu
rights: Copyright (c) 2025
date: 2025-01-10
publication date: 2025-01-10
starting page: 11598
ending page: 11606
doi: 10.15662/g665ez06

Comprehensive Audit Data Pipeline Architecture - Strategies for Modern Banking Audit, Compliance and Risk Management
type: item
description: 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.
subject: Articles
  topic: section
subject: Global Systemically Important Banks
  topic: keywords
subject: Risk Management
  topic: keywords
subject: Operational Resilience
  topic: keywords
subject: Python
  topic: keywords
subject: Alteryx
  topic: keywords
subject: Audit Data Pipeline
  topic: keywords
subject: Anomaly Detection Techniques
  topic: keywords
subject: Real-Time Streaming
  topic: keywords
subject: AI Models
  topic: keywords
creator: Mohan Kumar Sonne Gowda
rights: Copyright (c) 2025
date: 2025-01-10
publication date: 2025-01-10
starting page: 11590
ending page: 11597
doi: 10.15662/d31mt745

Leveraging Integrated Master Data and Claims Pipelines to Transform Medication Synchronization in Pharmacy Services
type: item
description: 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.
subject: Articles
  topic: section
subject: Pharmacy Services
  topic: keywords
subject: Data Aggregation
  topic: keywords
subject: Product Data Store (PDS)
  topic: keywords
subject: ETL
  topic: keywords
subject: Big Data Strategies
  topic: keywords
creator: Nagabhushanam Bheemisetty
rights: Copyright (c) 2025
date: 2025-01-10
publication date: 2025-01-10
starting page: 11581
ending page: 11589
doi: 10.15662/w184zm03

A Secure Micro-Frontend and Cloud Data Orchestration Architecture for Enterprise Web Platforms
type: item
description: 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.
subject: Articles
  topic: section
subject: Micro-Frontends
  topic: keywords
subject: Zero-Trust Security
  topic: keywords
subject: Secure Data Orchestration
  topic: keywords
subject: Policy-as-Code
  topic: keywords
subject: Enterprise Web Architecture
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Cloud Microservices
  topic: keywords
creator: Naveen babu Godavarthi
rights: Copyright (c) 2023
date: 2023-05-10
publication date: 2023-05-10
starting page: 8277
ending page: 8280
doi: 10.15662/IJARCST.2023.0603007

Smart Machines, Smarter Outcomes the Rise of Self-Learning Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Self-Learning Systems
  topic: keywords
subject: Fusion
  topic: keywords
subject: Meta-Learning
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Human-AI Collaboration
  topic: keywords
creator: Srikant Sudha Panda
rights: Copyright (c) 2023
date: 2023-10-11
publication date: 2023-10-11
starting page: 9004
ending page: 9015
doi: 10.15662/IJARCST.2023.0605009

The Evolving Landscape of Hardware and Firmware Engineering in Cloud Infrastructure
type: item
description: 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.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Big Data
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Automated Regression Testing
  topic: keywords
subject: Virtualized Simulations
  topic: keywords
subject: Agile Methodologies
  topic: keywords
subject: DevOps-driven Lifecycle
  topic: keywords
creator: Srikant Sudha Panda
rights: Copyright (c) 2025
date: 2025-08-06
publication date: 2025-08-06
starting page: 12473
ending page: 12484
doi: 10.15662/IJARCST.2025.0804005

Harnessing Artificial Intelligence for Salesforce Metadata Advanced Migration Strategies and Strategic Business Benefits
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Salesforce
  topic: keywords
subject: Regression Testing
  topic: keywords
subject: Data-Intensive Models
  topic: keywords
creator: Bijal Lalitkumar Dave
rights: Copyright (c) 2024
date: 2024-12-10
publication date: 2024-12-10
starting page: 11398
ending page: 11408
doi: 10.15662/IJARCST.2024.0706027

CYPRESS PERFORMANCE INSIGHTS PREDICTING UI TEST EXECUTION TIME USING COMPLEXITY METRICS
type: item
description: 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 unc…
subject: Articles
  topic: section
subject: Execution Time Prediction
  topic: keywords
subject: Machine Learning Regression
  topic: keywords
subject: Multi-layer Perceptron
  topic: keywords
subject: Test Automation
  topic: keywords
subject: Model Evaluation
  topic: keywords
subject: Software Testing Metrics
  topic: keywords
creator: Sudhakara Reddy Peram
creator: Praveen Kumar Kanumarlapudi
creator: Sridhar Reddy Kakulavaram
rights: Copyright (c) 2023
date: 2023-12-11
publication date: 2023-12-11
starting page: 9516
ending page: 9539
doi: 10.15662/IJARCST.2023.0606021

An AI-Driven Cloud Security Framework for Fraud Detection in Banking and Financial Markets Using Machine Learning and Deep Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: Fraud detection
  topic: keywords
subject: cloud security
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: financial markets
  topic: keywords
subject: PCI-DSS
  topic: keywords
subject: explainable AI
  topic: keywords
subject: streaming analytics
  topic: keywords
subject: concept drift
  topic: keywords
creator: Benjamin André Girard Thompson
rights: Copyright (c) 2024
date: 2024-05-23
publication date: 2024-05-23
starting page: 71
ending page: 78
doi: 10.15662/IJARCST.2024.0701810

Secure Healthcare Data Exchange and Financial Fraud Detection Using AI-Driven Cloud Data Integration
type: item
description: 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.
subject: Articles
  topic: section
subject: Healthcare data security
  topic: keywords
subject: cloud computing
  topic: keywords
subject: financial fraud detection
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: electronic health records
  topic: keywords
subject: data integration
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: anomaly detection
  topic: keywords
creator: Callum Patrick Harrington Rhodes
rights: Copyright (c) 2024
date: 2024-05-23
publication date: 2024-05-23
starting page: 67
ending page: 70
doi: 10.15662/IJARCST.2024.0701809

Next-Generation AI and Cloud Architecture for Scalable Fraud Detection in SAP HANA
type: item
description: 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.
subject: Articles
  topic: section
subject: AI-driven fraud detection
  topic: keywords
subject: SAP HANA Cloud
  topic: keywords
subject: cloud computing
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: banking systems
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: DevSecOps
  topic: keywords
creator: Simone Leonardo Moretti De Santis
rights: Copyright (c) 2024
date: 2024-05-23
publication date: 2024-05-23
starting page: 59
ending page: 64
doi: 10.15662/IJARCST.2024.0701808

A DevOps-Enabled AI Analytics Pipeline for Fraud Detection and Cyber Risk Mitigation in SAP HANA Cloud
type: item
description: 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.
subject: Articles
  topic: section
subject: DevOps
  topic: keywords
subject: SAP HANA Cloud
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Cyber Risk Mitigation
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: MLOps
  topic: keywords
subject: DevSecOps
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: AI Analytics
  topic: keywords
creator: Mathieu Olivier Charbonneau Giraud
rights: Copyright (c) 2024
date: 2024-05-23
publication date: 2024-05-23
starting page: 52
ending page: 58
doi: 10.15662/IJARCST.2024.0701807

Real-Time Fraud Detection and Cybersecurity in SAP HANA Using Machine Learning, Deep Learning, and DevSecOps Automation
type: item
description: 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.
subject: Articles
  topic: section
subject: SAP HANA
  topic: keywords
subject: fraud detection
  topic: keywords
subject: real-time analytics
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: DevSecOps
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: predictive analysis
  topic: keywords
creator: Marcus Stefan Norberg Hallström
rights: Copyright (c) 2024
date: 2024-05-22
publication date: 2024-05-22
starting page: 43
ending page: 51
doi: 10.15662/IJARCST.2024.0701806

AI-Driven Cyber Fraud Detection in Financial and Healthcare Ecosystems Using Cloud Deep Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: AI-driven fraud detection
  topic: keywords
subject: cloud computing
  topic: keywords
subject: deep learning
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: financial markets
  topic: keywords
subject: healthcare data security
  topic: keywords
subject: network security
  topic: keywords
subject: DevSecOps
  topic: keywords
creator: Declan Tiernan McLaughlin Reid
rights: Copyright (c) 2024
date: 2024-05-22
publication date: 2024-05-22
starting page: 34
ending page: 42
doi: 10.15662/zyk99r25

Intelligent Fraud Detection in Cloud Computing Using AI-Enabled Machine Learning and Network Security Analytics
type: item
description: 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.
subject: Articles
  topic: section
subject: Cloud security
  topic: keywords
subject: fraud detection
  topic: keywords
subject: machine learning
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: intrusion detection systems (IDS)
  topic: keywords
subject: network telemetry
  topic: keywords
subject: concept drift
  topic: keywords
subject: explainable AI
  topic: keywords
subject: adversarial robustness
  topic: keywords
subject: federated learning
  topic: keywords
creator: Oliver Patrick Whitfield Turner
rights: Copyright (c) 2024
date: 2024-05-22
publication date: 2024-05-22
starting page: 26
ending page: 33
doi: 10.15662/IJARCST.2024.0701804

Cloud-Native AI and Deep Learning Models for Real-Time Fraud Detection and Cybersecurity in Financial Institutions
type: item
description: 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.
subject: Articles
  topic: section
subject: Cloud-native
  topic: keywords
subject: fraud detection
  topic: keywords
subject: deep learning
  topic: keywords
subject: real-time
  topic: keywords
subject: streaming
  topic: keywords
subject: graph neural networks
  topic: keywords
subject: MLOps
  topic: keywords
subject: feature store
  topic: keywords
subject: explainability
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: financial institutions
  topic: keywords
subject: concept drift
  topic: keywords
subject: federated learning
  topic: keywords
creator: Florian Dietrich Silbermann
rights: Copyright (c) 2024
date: 2024-05-22
publication date: 2024-05-22
starting page: 17
ending page: 25
doi: 10.15662/IJARCST.2024.0701803

Driving Salesforce Testing Excellence with AI and Metadata-Driven Intelligent Automation
type: item
description: 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.
subject: Articles
  topic: section
subject: Salesforce
  topic: keywords
subject: CRM solution
  topic: keywords
subject: Smart Works 2.0
  topic: keywords
subject: AI-driven
  topic: keywords
subject: DevOps Pipeline
  topic: keywords
subject: Real-time Analytics
  topic: keywords
creator: Bijal Lalitkumar Dave
rights: Copyright (c) 2024
date: 2024-07-10
publication date: 2024-07-10
starting page: 10647
ending page: 10655
doi: 10.15662/IJARCST.2024.0704009