Knowledge Base Ontology Building for Fraud Detection Using Topic Modeling

Moving towards the digitization and cashless economy tests the existing IT infrastructure for security and fraud controls substantially. Transition from traditional to cashless economy requires to banks to have more secure system to fight fraud. To understand and transform the needs for more secure banking system it is necessary to understand the domain of fraud and create knowledge base for fraud. It helps bridge the gap between business level and IT levels of banking. So that anti-fraud regulations could be automatically imbibed in the system. Hence the paper focuses on analyzing existing fraud case documentations and understand the significant terms involved in the fraud. For this TF-IDF weighting, topic modeling with LDA is used for identifying the group of words (topic) representing particular type of fraud. Using these knowledge base ontology is extracted which can be used for building fraud detection system. Experiment is performed on extracted fraud documents and ontology is built using the latent topics identified.