Associated Professor, Amirkabir University of Technology
PhD Candidate of Industrial Engineering, Iran University of Science and Technology.
Assistant Professor, Amirkabir University of Technology
Insurance companies are faced with the challenge of money laundering. Money laundering is a complex, dynamic and distributed process which exposes insurance companies to legal, operational and reputational risks. Previous studies in insurance investigate the fraud in insurance and proposed different methods for fraud detection, while money laundering as a crucial phenomenon in insurance, which exposes the insurance company to risk, is neglected. We explore the money laundering in insurance and propose an efficient statistical method to detect it.
In this paper we propose a useful strategy which aimed at stratified sampling instead of exhaustive inspection for detecting money laundering activities. This approach is based on a division of insureds into homogeneous subgroups (strata). For this purpose, we firstly formulate the stratification task as a non-linear restricted optimization problem, in which the variance of overall amount of money laundered due to money laundering activities is minimized. Then we develop the metaheuristic approach namely the genetic algorithm (GA) to compute the optimum number of subgroups. The results show that the near optimum number of strata is 600, which means that we should divide insureds into 600 groups and survey these samples instead of surveying all insureds.