Improving fraud detection with semi-supervised topic modeling and keyword integration.

ABSTRACT: Fraud detection through auditors' manual review of accounting and financial records has traditionally relied on human experience and intuition. However, replicating this task using technological tools has represented a challenge for information security researchers. Natural language processing techniques, such as topic modeling, have been explored to extract information and categorize large sets of documents. Topic modeling, such as latent Dirichlet allocation (LDA) or non-negative matrix factorization (NMF), has recently gained popularity for discovering thematic structures in text collections. However, unsupervised topic modeling may not always produce the best results for specific tasks, such as fraud detection. Therefore, in the present work, we propose to use semi-supervised topic mod

SUBMITTER: Sanchez M 

PROVIDER: S-EPMC10803081 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature