International Journal of Human Computer Interaction and Data Mining

E-Procurement Governance and Fraud Detection in Digitally Integrated Public Institutions

2026-06-17T09:19:09+00:00

This study examines how e-procurement governance shapes fraud detection effectiveness across globally integrated public institutions by positioning digital control systems within a broader institutional accountability debate. Using an unbalanced panel of 130 to 160 economies from 2007 to 2025 and about 2,700 observations, the analysis applies fixed effects regression with interaction terms to estimate causal relationships between governance dimensions and fraud detection outcomes. The results indicate that procurement analytics oversight and digital procurement control exert the strongest positive effects, with correlations exceeding 0.73, while supplier data governance and transparency also yield statistically significant improvements in detection capacity. These effects operate through reduced information asymmetry, automated audit trails, and real-time anomaly detection systems that shift monitoring from reactive to proactive enforcement. The findings further reveal that institutional digital control capacity amplifies governance effects, indicating that cybersecurity readiness, audit competence, and enforcement strength condition effectiveness across contexts. The study advances governance theory by integrating control, transparency, data integrity, and analytics into a unified causal framework. The implications show that digital procurement reforms require aligned institutional capacity to deliver measurable integrity gains at scale.

How to cite this article:
Celestin M, Mishra A K. E-Procurement Governance and Fraud Detection in Digitally Integrated Public Institutions. Int J Hum Comp Inter Data Min. 2026; 9(2): 1-23.

DOI: https://www.doi.org/10.24321/3117.4841.202606

A Review on Machine Learning and Hybrid Ensemble Techniques for Money Laundering Detection in Financial Transactions

2026-06-30T07:48:28+00:00

Money laundering, a crime of severe financial dimension, tinges the tranquillity, transparency, and security of the global financial systems. Conventional anti-money laundering (AML) methodologies are to a large extent rule-based, making it considerably hard to detect ever-evolving, complex money-laundering patterns, thereby frequently making a final error. Now, with huge transactions on the digital banking sphere and the increased frequency in these transactions, clever data-driven mechanisms need to come into existence for identifying suspicious activity. This review walks through an extensive discussion on the detection of money laundering techniques through the use of rule-based systems, statistical methods, machine learning algorithms, or hybrid ensemble approaches. Machine learning approaches like logistic regression, decision tree, random forest, support vector machine, and XGBoost saw enhanced efficacy in making very good suggestions of patterns in the data of transactions. But these single models face problems with class imbalance, overfitting, and generalised data. Now, hybrid and ensemble learning models, which score better in single and multiple classifiers, are essential for ensuring accuracy, robustness, and reliability. Also, optimisation models such as genetic algorithms are given rings to improve the results of hyperparameter optimisation. The review also pays emphasis on good data preprocessing, feature extraction, and an essential ingredient in today's day—temporal data split and, in the end, performance metrics evaluation, which includes metrics like accuracy, precision, recall, F1 score, and ROC-AUC. This research has shown that a combination of the use of machine-learning models with ensemble machine-learning models and optimisation techniques is optimal for an extensible and efficient economic model for better day-front financial crime; it thus better targets financial crimes in real-world applications.

How to cite this article:
Mishra R, Rai A K. A Review on Machine Learning and Hybrid Ensemble Techniques for Money Laundering Detection in Financial Transactions. Int J Hum Comp Inter Data Min. 2026; 9(2): 24-32.

Artificial Intelligence for Customer Engagement in Digital Marketing

2026-08-04T11:59:02+00:00

Artificial intelligence (AI) is transforming the field of digital marketing by fundamentally improving the way organizations interact with their customers. AI technologies — including chatbots, recommendation systems, predictive analytics, and personalized marketing — help businesses understand customer behavior and deliver superior, data-driven experiences. This paper examines the role of AI in improving customer engagement in digital marketing. Customer engagement is a critical component of building trust, enhancing satisfaction, and increasing brand loyalty. AI enables marketers to analyze vast amounts of user data quickly and accurately, allowing creation of fully customized experiences based on individual interests, preferences, and online behavior. Despite improvements in performance and decision-making, challenges including data privacy, ethical concerns, and overdependence on technology remain significant. This research concludes that AI has become an essential tool to strengthen customer relationships and help companies remain competitive in the digital era.