Financial fraud detection by using Grammar-based multi-objective genetic programming with ensemble learning

Abstract

Financial fraud is a criminal act, which violates the law, rules or policy to gain unauthorized financial benefit. The major 
consequences are loss of billions of dollars each year, investor confidence or corporate reputation. A study area called Financial 
Fraud Detection (FFD) is obligatory, in order to prevent the destructive results caused by financial fraud. In this study, we 
propose a new method based on Grammar-based Genetic Programming (GBGP), multi-objectives optimization and ensemble learning for 
solving FFD problems. We comprehensively compare the proposed method with Logistic Regression (LR), Neural Networks (NNs), Support 
Vector Machine (SVM), Bayesian Networks (BNs), Decision Trees (DTs), AdaBoost, Bagging and LogitBoost on four FFD datasets. The 
experimental results showed the effectiveness of the new approach in the given FFD problems including two real-life problems. The 
major implications and significances of the study can concretely generalize for two points. First, it evaluates a number of data 
mining techniques by the given real-life classification problems. Second, it suggests a new method based on GBGP, NSGA-II and 
ensemble learning.