Patrizio De Girolamo
Unsupervised Outlier Detection from Financial Transaction Data.
Rel. Luca Cagliero, Marco Mellia, Flavio Giobergia. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2023
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Abstract
In an era where Internet usage is increasingly widespread and banking transactions are becoming simpler and more immediate, there is a growing need for financial institutions to develop robust cybersecurity strategies to protect both themselves and their users. Among these strategies, the one known as "Transaction Monitoring" plays a crucial role in the modern financial landscape as it involves analyzing large volumes of banking data to detect anomalous behaviors, thereby preventing potential criminal activities. Transaction monitoring can be modeled as a specific instance of outlier detection, where outliers represent suspicious transactions that need to be identified. This task is challenging on several fronts, firstly due to dataset imbalance, which consists of a clear predominance of normal transactions over anomalous ones.
Additionally, given our limited experience in the financial field, the identification of what constitutes anomalous behavior poses another issue
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