This study aims to examine dimensionality reduction techniques – principal component analysis (PCA), linear discriminant analysis (LDA) and t-distributed stochastic neighbor embedding (t-SNE) – and their application in detecting financial crime. The objective is to demonstrate how these methods address feature correlations, reduce data complexity and retain critical fraud indicators in high-dimensional data sets.
This study reviews the principles and challenges of PCA, LDA and t-SNE and applies them to a real-world data set of financial crime cases drawn from the SEC’s Accounting and Auditing Enforcement Releases. Models are trained and evaluated using stratified cross-validation, with performance compared across dimensionality reduction methods using multiple metrics.
Results indicate that PCA provides efficient linear reduction while preserving variance, LDA enhances supervised classification by maximizing class separability and t-SNE uncovers local patterns useful for anomaly detection. Together, these methods demonstrate measurable improvements in interpretability, computational efficiency and fraud detection performance.
This paper extends prior work by offering a comparative analysis of PCA, LDA and t-SNE in financial crime detection, bridging theoretical foundations with practical application. It highlights the implications for regulators and auditors, providing a replicable framework for applying dimensionality reduction in fraud analytics.
