Unsupervised Algorithms for Credit Fraud Anom...

Unsupervised Algorithms for Credit Fraud Anomaly Detection

Author Name : Maneesh Thallapaku, Vagvala Ruthvik, Sandeep Reddy, Naveen Reddy

ABSTRACT

Anomalous behavior detected from user data can be leveraged to detect frauds in online transactions using machine learning. Detecting outliers in high-dimensional data can be a challenging task. However, using unsupervised algorithms, we can detect non-conforming data points, even in multi-dimensional data. This paper intends to demonstrate the detection of fraudulent activity, based on the credit card data generated from multiple transactions. Various clustering algorithms have been used to generate comparative results, which are further evaluated using numerous standardized metrics.

Keywords: fraud detection, credit card fraud, anomaly detection, outlier, machine learning, clustering