Fraud detection is of great importance to financial institutions. This paper is concerned with the problem of finding outliers in time series financial data using Peer Group Analysis (PGA), which is an unsupervised technique for fraud detection. The objective of PGA is to characterize the expected pattern of behavior around the target sequence in terms of the behavior of similar objects, and th...

Financial institutions increasingly rely upon data-driven methods for developing fraud detection systems, which are able to automatically detect and block fraudulent transactions. From a machine learning perspective, the task of detecting suspicious transactions is binary classification problem therefore many techniques can be applied. Interpretability however utmost importance management have ...

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transacti...

INTRODUCTION As a conseguence of expansion of modern technology , the number and scenario of fraud are increasing dramatically. Therefore, the reputation blemish and losses caused are primary motivations for technologies and methodologies for fraud detection that have been applied successfully in some economic activities. The detection involves monitoring the behavior of users based on huge dat...

This paper is a continuation of previous paper where the imbalance dataset problem was solved by applying a proposed novel partitioning-undersampling technique. Then a proposed innovative Insurance Fraud Detection (IFD) models were designed using base-classifiers; Decision Tree, Support Vector Machine and Artificial Neural Network. This paper proposed an innovative insurance fraud detection mod...

Inductive learning and classification techniques have been applied in many problems in diverse areas. In this paper we describe an AI-based approach that combines inductive learning algorithms and meta-learning methods as a means to compute accurate classification models for detecting electronic fraud. Inductive learning algorithms are used to compute detectors of anomalous or errant behavior o...