Enhancing Financial Crime Detection through Data Science-Driven Transaction Monitoring: A Comprehensive Framework for Modern Financial Institutions

Shivam Tiwari ()

International Journal of Computing and Engineering, 2025, vol. 7, issue 13, 53 - 63

Abstract: Financial institutions face mounting challenges in detecting money laundering, terrorist financing, and fraudulent activities within increasingly complex global payment ecosystems. Traditional transaction monitoring systems rely heavily on static rule-based approaches that generate excessive false-positive alerts while failing to adapt to evolving criminal methodologies. The integration of advanced data science techniques, including machine learning algorithms, behavioral profiling, and network analytics, offers transformative potential for enhancing detection capabilities while improving operational efficiency. Behavioral profiling through unsupervised learning establishes individualized customer baselines that enable context-aware monitoring beyond generic thresholds. Dynamic risk scoring methodologies implement ensemble learning techniques to generate comprehensive assessments incorporating transactional attributes, temporal patterns, and contextual factors. Network analytics and graph-based algorithms reveal complex criminal relationships and coordinated activities that conventional systems cannot detect, enabling the identification of sophisticated money laundering schemes spanning multiple jurisdictions. Real-time anomaly detection systems process continuous transaction streams through advanced statistical methods and neural network architectures, providing instantaneous detection capabilities. Automated preliminary investigation tools optimize compliance workflows by conducting initial data gathering and creating comprehensive assessment packages, while intelligent alert prioritization algorithms rank suspicious activities according to threat severity. Implementation strategies address practical challenges, including regulatory compliance requirements, system interoperability constraints, and model governance frameworks. The transformation toward data science-driven monitoring systems represents a paradigmatic shift from reactive detection to proactive prevention, strengthening financial crime defenses while maintaining operational sustainability and regulatory compliance.

Keywords: Transaction Monitoring; Machine Learning; Financial Crime Detection; Network Analytics; Behavioral Profiling (search for similar items in EconPapers)
Date: 2025
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