Recent Trends in Data Mining and Business Forecasting (P-ISSN: 3139-3888)

Algorithmic Blockchain Tax Rebates via Central Bank Digital Currencies (CBDC) Automation

Sat, 04 Jul 2026 00:00:00 +0000

India’s agricultural sector is the backbone of its economy, yet the current system for distributing subsidies is often plagued by corruption, “ghost farmers,” and long delays. To solve this, the research proposes a system that combines the security of blockchain with India’s new Central Bank Digital Currency (CBDC). Instead of waiting months for paperwork to be processed, this framework uses smart contracts essentially self-executing digital rules to automatically verify a farmer’s eligibility and send money directly to their digital wallet in under 15 minutes. It introduces the Hybrid Confidential Financial Data Sharing Framework (HC-FDSF), which uses advanced math to keep personal details like Aadhaar and land records private while still allowing the government to audit transactions for fairness. By removing middlemen and automating the math behind tax rebates and payouts, the system could cut fund leakages by over 95%. Although challenges like digital literacy and technological integration remain, this study provides a blueprint for a faster, more transparent financial ecosystem. Ultimately, this research shows how technology can ensure that every eligible farmer receives their entitled benefits instantly and securely.

Driving Enterprise Efficiency through Digital Transformation: Evidence from Intelligent Automation and Cloud-Based Business Ecosystems

Ankit Jain Shastri — Thu, 06 Aug 2026 00:00:00 +0000

The continuous acceleration of digital technologies has fundamentally reconfigured how contemporary organizations design operations, compete in dynamic markets, and deliver customer value. Across diverse industry domains, enterprises are increasingly deploying intelligent automation, cloud computing platforms, and interconnected digital ecosystems to optimize operational workflows, bolster customer experience, and build long-term organizational resilience. This study investigates how digital transformation drives enterprise efficiency through the strategic deployment of intelligent automation and cloud-enabled business architectures. Utilizing a multi-theoretical framework anchored in the Technology–Organization–Environment (TOE) Framework, Socio-Technical Systems Theory, and the Technology Acceptance Model (TAM), the paper establishes a detailed conceptual understanding of the mechanisms linking digital innovation to elevated organizational performance. To complement theoretical perspectives, this study conducts empirical comparative case analyses of two enterprise initiatives: the FYI Booking Platform and the JustHealth Platform. These case studies highlight the transformational impact of cloud-native infrastructure, automated workflow orchestration, centralized information management, and data-driven decision-making systems in driving operational excellence. The findings indicate that technological deployment alone is insufficient; superior performance, scalability, quality improvements, and sustainable cost reductions are realized primarily when organizations harmoniously align technological tools with human capital, process redesign, and cultural readiness.

Data Mining and Predictive Analytics for Business Forecasting: Emerging Trends and Applications in Digital Commerce

M. Arunkumar Victor, M. Manida — Mon, 19 Oct 2026 00:00:00 +0000

The rapid growth of digital commerce has generated vast volumes of structured and unstructured data from online transactions, customer interactions, digital payments, social media, and e-commerce platforms. Data mining and predictive analytics have emerged as important tools for transforming these data into meaningful business insights and improving forecasting accuracy. This paper examines the emerging trends and applications of data mining and predictive analytics in business forecasting, with particular reference to digital commerce. The study focuses on the application of data-driven techniques for sales forecasting, customer behaviour prediction, demand estimation, market trend analysis, inventory planning, customer segmentation, and revenue management. It highlights the growing use of machine learning, artificial intelligence, big data analytics, and real-time data processing in supporting strategic and operational decision-making. The paper also discusses how predictive models can help digital businesses identify emerging market patterns, anticipate customer requirements, manage business risks, and improve resource allocation. Particular attention is given to the integration of transactional, behavioural, and digital interaction data for developing more responsive forecasting models. The study adopts a conceptual and descriptive approach based on secondary sources, including research articles, industry reports, and relevant academic literature. The findings indicate that the integration of data mining and predictive analytics can enhance forecasting capabilities and support evidence-based decision-making in digital commerce. However, issues relating to data quality, privacy, cybersecurity, algorithmic bias, technological capability, and analytical skills remain important challenges. The paper concludes that responsible adoption of advanced analytics can strengthen business agility, competitiveness, and sustainable growth in the evolving digital commerce environment.

Predictive Modelling of FDI Trends Leveraging Time-Series Data to Forecast the Impact of Political Stability in Emerging Markets

Tue, 27 Oct 2026 00:00:00 +0000

Primarily in the developing economies of South Asia, political stability has become a crucial component in securing foreign direct investment (FDI). The main objective of this research paper is to evaluate the impact of political stability on Foreign Direct Investment (FDI) inflows in three selected South Asian countries (Sri Lanka, Pakistan, and Bangladesh) through a quantitative research design and a deductive approach for the period of 2000-2022. The Autoregressive Distributed Lag (ARDL) model, which is suitable in the presence of mixed orders of integration, is used in this research to capture both short-run and long-run dynamics. The dependent variable in this study is foreign direct investment, with political stability acting as the primary independent variable. Additionally, four control variables are included: government spending, inflation, openness to trade, and corruption control. The model's findings show that in some South Asian nations, political stability positively affects foreign direct investment inflow. While government spending is not even marginally significant, the other control variables, such as inflation, openness to trade, and corruption control, have substantial positive and negative correlations. Therefore, it can be concluded that in order to draw in more Foreign Direct Investment (FDI) inflows, South Asian nations need to improve their political stability by putting in place suitable laws and procedures.