My second research endeavor, titled "Financial Fraud Detection within Financial Sub-networks using Unsupervised Machine Learning Algorithms," was presented at an IEEE conference and is also indexed in SCOPUS. This study explores the application of unsupervised machine learning techniques to detect fraudulent activities within financial sub-networks, a critical issue for financial institutions and networks. The work leverages unsupervised algorithms to uncover hidden patterns and anomalies in financial data, enabling early identification of potential fraudulent behaviors without relying on labeled data. This research contributes to the growing field of financial fraud detection by offering a scalable, data-driven solution that can be applied to real-world financial systems. The findings have significant implications for enhancing security and trust within financial networks, providing businesses with the tools to better protect their assets and ensure the integrity of transactions. This paper highlights my ability to apply machine learning to solve complex, real-world challenges in the financial sector.
Link to the published Research Paper in the Scopus-Indexed Conference: https://ieeexplore.ieee.org/document/10390325