Abstract

Risk scoring algorithms are central to securing transactional activities in digital financial platforms by quantifying the likelihood of fraudulent, anomalous, or malicious behavior. In the context of increasing volumes of real-time transactions, diverse user profiles, and sophisticated attack vectors, these algorithms support fraud detection, anti-money laundering (AML) compliance, and risk-based authentication. This paper investigates the theoretical foundations, algorithmic structures, evaluation metrics, and practical deployments of risk scoring models tailored for transactional security. We analyze traditional statistical methods, machine learning classifiers, ensemble techniques, and advanced deep learning architectures, situating them within real-world financial environments. The research methodology outlines a comprehensive experimental framework that incorporates dataset selection, feature engineering, model training, validation, and deployment considerations, with sensitivity analysis and performance metrics including accuracy, precision, recall, AUC, and false positive rates. We further synthesize the advantages and disadvantages of various approaches, noting challenges related to imbalance, interpretability, computational cost, and adversarial evasion. Results and discussion articulate empirical findings, comparative performance, and operational implications for risk scoring in digital finance. The conclusion consolidates insights for practitioners and researchers, while future directions highlight explainability, federated learning, adaptive models, and privacy-preserving techniques. This survey aims to inform effective design and evaluation of risk scoring systems for transactional security in evolving financial ecosystems