Fintech: Self Organizing Maps for Fraud Detection
DOI:
https://doi.org/10.48001/978-81-966500-3-2-9Keywords:
Financial Fraud, Detection, Deep Learning, Fintech, Self-Organizing MapsAbstract
Our relationships with the outside world have changed as a result of digitalization, which has also created new growth potential and drastically changed the banking sector. Large volumes of data were created as banks made the switch to digital operations; as of right now, the internet contains more than 44 zettabytes of data. This change brought about new vulnerabilities and enhanced efficiency, but it also exposed the financial sector to never-before-seen levels of fraud. In order to overcome this difficulty, machine learning becomes a vital instrument for spotting and stopping fraud. Large, precisely labeled datasets are necessary for standard machine learning techniques, but obtaining them can be challenging and time-consuming. This problem is avoided by deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which learn from raw data without explicit labeling. This allows for the development of reliable fraud detection systems. This chapter provides an account on Self-Organizing Maps (SOMs), a powerful deep-learning method that performs exceptionally well in grouping and dimensionality reduction.
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