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PARTICULAR
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Page No.
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Evaluating Model-Based Products in Enterprise Program Management: A Governance and Decision Framework for Technical Program and Product Managers
Prakash Achuthan
DOI:18.A003.ijmra.2026.J1501.10.28945
Abstract:
Model-based products powered by artificial intelligence and large language models generate probabilistic outputs that introduce reliability risk, cost variability, performance drift, explainability limitations, data dependency, and organizational adoption challenges [1][2].
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1-6
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AI Governance in Large-Scale Enterprise Financial Systems: A Multilayer Framework for Risk, Compliance, and Trust
Sudeep Agarwal
DOI:18.A003.ijmra.2026.J1501.10.28946
Abstract:
Artificial intelligence (AI) is increasingly embedded in large-scale enterprise financial systems, where it supports credit decisioning, fraud detection, risk management, and regulatory reporting.
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7-15
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ARTIFICIAL INTELLIGENCE AND THE NATIONAL EDUCATION POLICY (NEP) 2020: A QUALITATIVE EXPLORATION OF INTEGRATION, OPPORTUNITIES, AND CHALLENGES
Dr.T.Mohana Sundari
DOI:18.A003.ijmra.2026.J1501.10.28947
Abstract:
The integration of Artificial Intelligence(AI) into education has become a defining feature of
21st-century pedagogical transformation. AI technologies such as intelligent tutoring systems,
adaptive learning platforms, and predictive analytics are reshaping teaching and learning into
more personalized, data-driven processes.
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16-22
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Impact of Perception Factors and Awareness on Mutual Fund Investment Decisions
Dr.Kshama Ganjiwale , Dr.Sandeep Malu
DOI:18.A003.ijmra.2026.J1501.10.28948
Abstract:
Mutual funds have become a popular investment option due to their diversification and professional management benefits. However, investment decisions are largely influenced by investors’ perception, awareness, and behavioural factors. This study aims to examine the impact of various perception factors on mutual fund investment decisions and to analyse the level of awareness among investors.
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23-32
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A Graph Neural Network-Enabled Machine Learning Framework for Secure Financial Fraud Detection
Dr. P.S. Thakur
DOI:18.A003.ijmra.2026.J1501.10.28949
Abstract:
Financial fraud causes global losses exceeding USD 5.1 trillion annually, with the rapid digitization of financial services continuously expanding the attack surface available to malicious actors. Conventional rule-based and classical machine learning fraud detection systems suffer from two fundamental shortcomings: they operate on isolated transaction records without capturing relational patterns among accounts, and they exhibit poor adaptability to the adversarial concept drift characteristic of sophisticated fraud rings.
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33-40
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