International Journal of Intelligent Systems and Applications in Engineering

Secure AI-Driven Identity Infrastructure for Regulated Sectors

Pramod Gannavarapu — 2025-11-12

The regulated industry is facing increased threats to identity as attacks on fleeting clouds, APIs, and mobile devices increase the attack surface. To provide real-time monitoring, adaptive authentication, and auditable compliance, this paper suggests a secure and AI-driven identity infrastructure, a fusion of ML, NLP, and LLM-driven analytics with zero-trust design. The work was inspired by the fact that identity-related breaches are prevalent in most breaches, including the hack of Global data breach victims in 2023 by a total of 43% of all government breaches, which are common, particularly in healthcare, finance, and government ecosystems, where HIPAA, GDPR, and SOX control identity theft. The architecture employs authentication, authorization, entitlement, and evidence in restricted, microservice settings; expels operational telemetry into online feature stores; and implements policy-as-code in a hybrid cloud. Approaches combine scaling risk scoring with gradient-boosting session anomaly sequence models, toxic entitlement graph analytics, and federated learning to minimize data movement. Covering 100,000 sessions in four weeks, the stack outperforms rules-based baselines on metrics including accuracy, recall, F1, ROC-AUC, median arena decision latency, coverage of compliance, newly seen evidence, and false challenges. Findings show that 92 out of 100 fraud attempts were reported caught in 30 seconds (vs. a 60-second baseline), resulting in 31 fewer attempted frauds, a 19-point improvement in coverage of evidence, and the availability of decisions at 99.6% in stressful situations. Up to 35% of fraud was reduced, and the user experience was maintained at a much higher level using adaptive authentication. The study concludes by discussing governance, privacy-saving methods, and a research agenda focused on standard benchmarks, compliance, and adaptive compliance.

DOI: https://doi.org/10.17762/ijisae.v13i2s.7913

Scalable Data-Driven Engineering for High-Performance Computing & Financial Services

Santosh Durgam — 2025-11-12

The study discussions will touch upon enhancements to the efficiency of high-performance computing systems through the assimilation of scalable data engineering and semiconductor verification to improve data computing technological processes, especially in the financial services sector. The authors of this study explore how computerized gross working of applications like fraud detection, risk control, and algorithmic trading can be optimized through the integration of GPU hardware verification with AI-ready systems. The major results of the study include that the performance of the system increased by 25%, and the time spent on studying cases decreased as the proposals of the GPU made it possible, and the provisions of AI-driven hardware verification to conduct the verification were enhanced by 30%. Moreover, it also found data engineering pipelines to compute 95% success rates when handling real-time financial transactions, and with latency reduced to under <5ms. A combination of scaled, automated data streams has led to cost reductions of 15-20% per year, and the use of machine learning in semiconductor testing has shown that the decrease in tests could be up to 90%. The paper concludes that convergence of hardware validation and scalable data engineering is a key to devising AI-ready systems, capable of meeting the computational needs of the present financial services, enhancing reliability, scalability, and velocity. The integration is set to drive the real-time ability of decisions in the economic field to a greater extent.

DOI: https://doi.org/10.17762/ijisae.v13i2s.7914

Cloud-Powered Healthcare & Insurance Transformation with CRM and Advanced Analytics

Sridhar Rangu — 2025-11-12

Healthcare and insurance agencies are experiencing disjointed experiences, an increase in service demands, and stringent regulations that hinder cost, quality, and expertise. The paper presents a proposal of a cloud-native blueprint that will merge Salesforce Health Cloud, Service Cloud Voice, and Einstein Copilot with a controlled analytics stack to modernize engagement, care coordination, and claims. Data is extracted through FHIR/HL7 and APIs into a lakehouse and feature store and operated to serve risk, service, and fraud models and operationalized within a CRM component based on retrieval-augmented guardrails. They are entity resolution to a Member/Patient 360, PHI tokenization, and experiment-ready instrumentation. With expected results of 10-20% decrease in Average Handle Time, a 6-10 percentage point increase in First-Contact Resolution, a 15 percent reduction in claims cycle time, readmission AUROC >0.82, precision of SIU logged at 1k ≥0.60, and ≥99.9% logged with ≤0.1% policy exceptions, steps A/B were hired, the randomized-encouragement and threshold A/B designs are applied. Observability focuses on P95 API latency of <300 ms and ASR WER under 12% and the cost per member per month is regulated and taxed to be between $0.08–$0.25 per month. The donation will include a Remote deployable compliant reference architecture, measurement plan, connecting the model measures to business KPIs, and guardrails on fairness, safety, and reliability. The solution can be applied to federated learning, multimodal analytics, and streaming interoperability through FHIR Subscriptions and payer-to-payer APIs. Research is applicable across both payers and providers and contributes to a gradual rollout and clear governance, financial, and well-defined performance WM.

DOI: https://doi.org/10.17762/ijisae.v13i2s.7915