Journal of Advanced Research in Dynamic Financial Risk Assessment and Investment Strategies

Cooperative Tourism Information Center for Establishing all credit and non credit Cooperative institutions in Kerala: Execution and Implementation

2026-03-24T09:45:27+00:00

Kerala’s cooperative tourism sector has increasingly sought to integrate cooperative banking infrastructure with Cooperative Tourism Information Centres (CTICs) to foster inclusive economic growth, financial inclusion, and resilient community-based tourism. This study investigates the conceptual foundations, implementation strategies, and operational challenges of this integration, with a focus on how CTICs can function as trusted nodes for digital economic exchange, information aggregation, and local empowerment. Drawing on interdisciplinary frameworks—including cooperative banking, spatial economics, grassroots digital currencies, and trusted information systems—the research synthesises empirical evidence, institutional innovations, and digital protocols to contextualise Kerala’s model within global practices. Comparative insights from French cooperative banks, grassroots currency systems, and AI-driven information architectures inform the analysis, highlighting opportunities for scalable, secure, and participatory tourism governance. The findings underscore the potential of hybrid cooperative-financial structures to enable democratised information management, strengthen community engagement, and catalyse localised digital economies.

Leveraging Generative AI for Enhancing GST Compliance Efficiency: A Secondary Study of MSMEs in Gujarat

2026-03-24T10:06:28+00:00

The introduction of the Goods and Services Tax (GST) in India aimed to simplify the indirect tax system and improve transparency across businesses. However, Micro, Small, and Medium Enterprises (MSMEs), particularly in Gujarat, continue to face significant challenges in GST compliance, including complex return filing procedures, invoice mismatches, and frequent regulatory updates. These issues increase operational costs and create inefficiencies in financial management.

This study is based on secondary research and examines how generative artificial intelligence (AI) can improve GST compliance efficiency among MSMEs. The research analyses existing literature, industry reports, and government publications to understand current compliance challenges and the role of AI-driven technologies in addressing them. Generative AI, as an advanced form of artificial intelligence, enables automation of tasks such as invoice processing, tax classification, anomaly detection, and compliance reporting.

The findings suggest that AI-powered systems can significantly reduce manual errors, enhance accuracy in tax filings, and enable real-time monitoring of compliance activities. Additionally, these technologies support predictive risk analysis, allowing businesses to identify potential compliance issues in advance. Despite these benefits, the adoption of AI among MSMEs remains limited due to factors such as lack of awareness, high implementation costs, and limited digital infrastructure.

The study concludes that generative AI has strong potential to transform GST compliance processes for MSMEs by improving efficiency, reducing costs, and enabling proactive compliance management. It also highlights the need for supportive policies, awareness programmes, and affordable AI solutions to encourage wider adoption among small businesses.

A Secondary Study on the Effectiveness of Hybrid Human–AI Models in Enhancing Credit Risk Assessment in the Banking Sector with Special Reference to State Bank of India

2026-03-24T10:10:33+00:00

Credit risk assessment is a fundamental function in the banking sector, directly influencing financial stability and lending decisions. Traditional credit evaluation methods, primarily based on financial ratios, credit history, and human judgement, often face limitations such as subjectivity, slower processing, and inability to analyse large and complex datasets. With the rapid advancement of artificial intelligence (AI) and machine learning (ML), banks are increasingly adopting data-driven approaches to improve the accuracy and efficiency of credit risk prediction.

This secondary research paper aims to examine the effectiveness of hybrid human–AI models in enhancing credit risk assessment, particularly in the context of large public sector banks such as the State Bank of India (SBI). The study is based on an extensive review of existing literature, research journals, industry reports, and financial publications. It explores how AI-based models, including logistic regression, decision trees, and neural networks, outperform traditional methods in identifying patterns and predicting loan defaults.

However, the study also highlights the limitations of fully automated systems, such as lack of transparency, algorithmic bias, and reduced accountability. To address these challenges, the paper emphasises the importance of integrating human expertise with AI systems. A hybrid model enables banks to combine machine efficiency with human judgement, leading to more accurate, fair, and reliable credit decisions.

The findings suggest that hybrid Human–AI models can significantly improve credit risk management, reduce non-performing assets (NPAs), and enhance overall decision-making efficiency in the banking sector.

Advances in Quantitative and AI-Driven Approaches for Financial Risk Management and Portfolio Optimization: A Review

2026-03-24T10:13:27+00:00

The field of financial risk management and portfolio optimization has undergone a profound transformation with the integration of quantitative models and artificial intelligence (AI) techniques. Traditional approaches, such as mean–variance optimization, capital asset pricing models (CAPM), and risk parity, have long provided foundational frameworks for investment decision-making and risk assessment. However, these methods often face limitations in addressing nonlinear relationships, dynamic market conditions, high-dimensional datasets, and complex dependencies among assets. Recent advancements in AI—including machine learning, deep learning, and reinforcement learning—have enabled more sophisticated, data-driven approaches to both risk management and portfolio optimization. These AI-driven techniques allow for enhanced predictive accuracy, dynamic portfolio adjustment, real-time risk monitoring, and improved risk-adjusted returns across diverse financial markets.

This review synthesizes the latest research on AI applications in finance, highlighting their role in credit risk assessment, market risk prediction, algorithmic trading, and portfolio diversification. It also discusses the challenges associated with AI adoption, such as model interpretability, overfitting, data quality and availability, computational complexity, and compliance with regulatory standards. Furthermore, the paper identifies emerging trends, including hybrid models that combine traditional quantitative methods with AI, explainable AI (XAI) for transparent decision-making, and sustainable finance applications integrating environmental, social, and governance (ESG) criteria into portfolio strategies.

How to Get Investable Funds Moving?

2026-04-22T05:01:21+00:00

Mobilising large amounts of investable cash is still a major problem for financing development priorities and attaining long-term economic growth. There is a lot of money available around the world, but there are also big gaps between the money that is accessible and the investment opportunities that are actually worth pursuing, especially in emerging and underdeveloped countries. This essay looks at the structural, institutional, and market-based constraints that make it hard to effectively mobilise investible capital and suggests a strategy framework for getting beyond them. Based on current research, case studies, and policy analysis, the paper shows how financial intermediation, risk mitigation tools, regulatory clarity, and investor trust may all help get money flowing. It also looks into how blended finance, public-private partnerships, and new financial tools can help match risk-return profiles with what investors want. The essay says that for mobilisation to work, not only does the availability of capital need to go up, but project preparation, transparency, and governance structures all need to get stronger. The suggested approach provides actionable insights for policymakers, financial institutions, and development actors aiming to direct investable funds towards productive and impactful initiatives by merging governmental changes with market-driven solutions.