Arpit Garg

Sri Balaji Society, PGDM Telecom, Alumnus

NORTH MAHARASHTRA UNIVERSITY JALGAON, Electronics & Telecommunication, Alumnus

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Arpit Garg has experience in Technical Project Manager and Business Architect with over 17 years of experience in the Banking and IT/Telecom Industry (quote to cash, trouble to resolve customer journeys). Dedicated to delivering by leading, building, and maintaining strategic solutions leveraging the agile methodology framework that drives results, specializing in spearheading high-cost-saving migration projects through cutting-edge technology implementation while aligning with industry standards like eTOM, IFX, and BIAN.
He has the following certifications: Certified Business Analyst Professional (CBAP), Project Management Professional (PMP), Salesforce Admin 201, Certified Scrum Master (CSM), Azure AI Fundamental, Service NOW Admin, AWS Cloud Practitioner.
He is Artificial Intelligence, Machine Learning evangelist and serves as Author/Researcher, Reviewer at various conferences affiliated by IEEE and Springer. He has also participated as one of the Judges for various awards in the same space.
Address: Pittsburgh, PA

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Papers by Arpit Garg

Artificial Intelligence in Telecommunications: Applications, Risks, and Governance in the 5G and Beyond Era

International Journal of Computer Techniques, 2023

The incorporation of Artificial Intelligence (AI) has changed the telecom industry in terms of de... more The incorporation of Artificial Intelligence (AI) has changed the telecom industry in terms of designing, managing, and delivering services by telecom operators. As the industry gears up for 5G and looks toward 6G, AI techniques, such as machine learning, natural language processing, and predictive analytics, are being used to create more intelligent network operations, better customer experience, and more agile service delivery. Telecom operators are working with AI for automating fault detection, rescue allocation, service personalization, and revenue assurance. But on top of it, there is a new layer of challenges introduced: algorithmic transparency, data privacy, AI model biases, infrastructure readiness challenges, and the growing digital skills gap. Further complicating matters are regulatory uncertainties and the steep costs of implementation. This paper delves into how AI is being deployed across key telecom areas and the inherent technological and ethical risks it poses, then outlines future AI trends expected to shape next-generation networks. Using recent case studies, industry reports, and peer-reviewed research, the study explores AI as an enabler of operational change and as a disruptor of strategic change. Within this framework, a multi-layered approach into responsible AI adoption in telecom is proposed that tries to balance innovation and governance. This paper contains five tables and five figures (Python code-generated and conceptual diagrams) portraying the present state of applications, risk matrices, and investment trends in AI. Finally, the paper sets out a seed list of recommendations for telecom operators to scale AI-driven operations efficaciously while fortifying resilience, transparency, and accountability throughout the digital infrastructure.

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A Multi-Layered AI-IoT Framework for Adaptive Financial Services

International Journal of Emerging Trends in Computer Science and Information Technology, 2024

The Internet of Things (IoT) and Artificial Intelligence (AI) convergence is transforming the fin... more The Internet of Things (IoT) and Artificial Intelligence (AI) convergence is transforming the financial services domain and providing unprecedented capacities to deliver intelligent, secure, and hyper-personalized banking experiences. Through IoT, data is gathered in real time via networked devices such as wearables, smart ATMs, or mobile sensors; AI then feeds on this data to give predictive insights, risk assessments, or recommendations. This synergy now studies contextual digital banking services, automated decision-making, and real-time risk mitigation. The paper proposes a holistic framework that integrates streaming IoT data and AI-enabled analytics to support the services of the next-generation banks. The framework was developed using the design science methodology. It consists of three layers-Input, Intelligence, and Experience with the underlying principles of edge computing, federated learning, and secure identity management. Practical use cases are analyzed to demonstrate the working feasibility of this convergence: emotion-aware interfaces, personalized credit scoring, and real-time fraud detection. Key security and privacy issues inherited from deploying such interconnected and autonomous systems are studied, with possible solutions engaging blockchain, zero-trust architecture, and decentralized identity. Lastly, the paper assesses the business impact of the AI-IoT fusion in terms of operational efficiency, customer retention, and ROI on innovation. It is established that IoT and AI convergence is not just a technological improvement; this is now a strategic evolution toward ambient, autonomous, and adaptive financial services.

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Behavioral Biometrics for IoT Security: A Machine Learning Framework for Smart Homes

Journal of Recent Trends in Computer Science and Engineering (JRTCSE), 2022

The exponential proliferation of IoT devices and smart home technologies has posed fresh challeng... more The exponential proliferation of IoT devices and smart home technologies has posed fresh challenges in controlling secure access. Outdated mechanisms of access-using passwords, keyfobs, or biometrics-have become inadequate in tackling threats that constantly change and evolve within the heterogeneous and interconnected environments. Thus, ML has been recognized to provide a promising paradigm to strengthen access control mechanisms, to enforce dynamic adaptability, and to realize real-time anomaly detection and behavior-based decision-making powers. This paper conducts extensive research into machine learning-based access control systems for smart home and IoT environments. The limitations of traditional access control methods are discussed, thus underscoring the need for intelligent, automated systems that can learn contextual access behaviors. The second part of the study conducts a thorough literature review that reinforces the advantages of ML models, including Random Forest, CNNs, Autoencoders, and Federated Learning frameworks, for assisting legitimate users while detecting anomalies in real time [3], [5], [6], [10], [28]. Proposed is a concept integrating supervised and unsupervised learning models into the access control framework to be trained on benchmark datasets such as IoTID20 and Bot-IoT, with evaluation metrics including accuracy, precision, recall, and F1-score, therefore showing a significant improvement upon detection performance compared to legacy systems. The highest accuracy of 96.1% was achieved by CNNs, whereas Autoencoders did have good anomaly detection capabilities with the lowest training overhead. Moreover, the smart door lock case study allows evaluating real-world applicability and latency performance. Finally, the work describes emerging trends such as privacy-preserving learning, XAI, lightweight edge inference, and 6G-enabled IoT security architecture integration. The studies confirmed and validated that ML-powered access control systems can help to secure IoT while making scalable, adaptive, and intelligent smart home infrastructures. This study offers a working, modular design for the integration of ML-assisted authentication into future-proof smart environments.

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CNN-Based Image Validation for ESG Reporting: An Explainable AI and Blockchain Approach

FinTech platforms have been forced to justify the authenticity of their environmental claims. Yet... more FinTech platforms have been forced to justify the authenticity of their environmental claims. Yet, the existing auditing processes, which are overwhelmingly dominated by manual inspections and document-based verifications, have proved insufficient in guaranteeing transparency, speed, or scale. The matter is more acute when the data to be validated The framework can ingest multi-source visual inputs, including satellite imagery and drone views, alongside on-site IoT camera feeds and investor-submitted images, to assess the environmental compliance of a project remotely and almost instantaneously. Domain-specific datasets train deep learning models to capture environmental indicators, which include the placement of renewable infrastructure, illegal land clearing, water contamination, and fraudulent waste disposal. Performance analysis illustrates that the classification accuracy of over 92% and precision rate surpassing 90% can be achieved by the CNN-based system when it is used for the identification of non-compliant activities. In order to make the decision boundaries of the model interpretable-a key requirement for enhancing transparency to regulators and stakeholders-XAI methods, including SHAP and Grad-CAM, are integrated into the system. For audit-result integrity and traceability, outputs of audits are recorded in a permissioned blockchain using smart contracts that automatically deliver alerts and reports. Regulatory

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How Natural Language Processing Framework Automate Business Requirement Elicitation

International Journal of Computer Trends and Technology, 2025

Requirement elicitation is an important and critical phase in the software development lifecycle,... more Requirement elicitation is an important and critical phase in the software development lifecycle, but it is exposed to inefficiencies and human error as it is dependent on manual methods such as interviews, workshops, and document analysis [1-4]. With the growing complexity of modern systems and the increasing volume of unstructured data, there is a need for innovative solutions to address these challenges. This paper explores the use of Natural Language Processing (NLP) to automate some important aspects of the requirement elicitation process by using advanced NLP techniques, such as the Named Entity Recognition (NER) framework, which aims to extract relevant business requirements from various unstructured sources, including emails, transcripts, and documents. This automation improves the accuracy and traceability of the elicited requirements by reducing the manual effort required. This paper, by taking an example of a banking use case, demonstrates how this framework helped in not only reducing the time spent on the requirement elicitation significantly but also discovering a lot of undocumented yet important requirements. This paper also highlights the challenges and importance of human oversight with this framework in regulated industries. Finally, the paper concludes by providing future direction/guidance, including how AI systems can be integrated into the core ecosystem and help with interactive requirement elicitation.

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Unified Framework of Blockchain and AI for Business Intelligence in Modern Banking

INTERNATIONAL JOURNAL OF EMERGING RESEARCH IN ENGINEERING AND TECHNOLOGY, 2022

Combining blockchain with AI is heavily transforming digital banking by facilitating intelligent,... more Combining blockchain with AI is heavily transforming digital banking by facilitating intelligent, secure, and realtime decision-making processes. While financial institutions move away from legacy systems toward data-driven platforms, there is a growing need for real-time BI. Most transitional BI tools are thus limited by the presence of centralized data silos, slow data pipelines, and lack of transparency. In comparison, blockchain ensures a decentralized tamper-proof ledger infrastructure that gives assurances of data integrity, traceability, and auditability, whereas AI offers tools for extracting actionable insights such as predictive analytics, anomaly detection, and natural language processing. In pausing this study turns its focus on the synergistic integration of blockchain and AI toward real-time BI framework developments within digital banking ecosystems. A multi-layered architecture is thereby proposed wherein blockchain captures, validates, and stores transactional and behavioral data, whereas a layer of AI modules sit atop this secured data layer to generate intelligent patterns in real time. This research puts forward supervised learning models such as XGBoost and LSTM for fraud prediction and customer segmentation, while smart contracts trigger compliance workflows and rule-based alerts. Explainable AI techniques (e.g. SHAP, LIME) are also integrated for purposes of interpretability and regulatory compliance. Results indicate that fraud detection accuracy has been improved to 96%, latency to real-time insight generation has dropped substantially to a negligible level, and trust in AI results has been strengthened by the transparency of blockchain logging. Case studies of customer behavior analytics, transaction anomaly monitoring, and credit scoring show how this integrated approach outperforms traditional data infrastructures. Besides, this work has put forward other discussions on challenges in implementation such as interoperability, data privacy, computational costs, and regulatory acceptance. This research contributes to the fast-evolving discourse on digital transformation in finance, offering a scalable, secure, and interpretable blueprint for next-generation banking systems, which will take advantage of blockchain and AI in providing real-time intelligence.

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Arpit Garg

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