Applying Simulation to the Problem of Detecting Financial Fraud
Thesis available HERE
2016-10-28, J1650, Blekinge Institute of Technology, Karlskrona, 10:00 (English)
This thesis introduces a financial simulation model covering two related financial domains: Mobile Payments and Retail Stores systems.
The problem we address in these domains is different types of fraud. We limit ourselves to isolated cases of relatively straightforward fraud. However, in this thesis the ultimate aim is to introduce our approach towards the use of computer simulation for fraud detection and its applications in financial domains. Fraud is an important problem that impact the whole economy. Currently, there is a lack of public research into the detection of fraud. One important reason is the lack of transaction data which is often sensitive. To address this problem we present a mobile money Payment Simulator (PaySim) and Retail Store Simulator (RetSim), which allow us to generate synthetic transactional data that contains both: normal customer behaviour and fraudulent behaviour.
These simulations are Multi Agent-Based Simulations (MABS) and were calibrated using real data from financial transactions. We developed agents that represent the clients and merchants in PaySim and customers and salesmen in RetSim. The normal behaviour was based on behaviour observed in data from the field, and is codified in the agents as rules of transactions and interaction between clients and merchants, or customers and salesmen. Some of these agents were intentionally designed to act fraudulently, based on observed patterns of real fraud. We introduced known signatures of fraud in our model and simulations to test and evaluate our fraud detection methods. The resulting behaviour of the agents generate a synthetic log of all transactions as a result of the simulation. This synthetic data can be used to further advance fraud detection research, without leaking sensitive information about the underlying data or breaking any non-disclose agreements.
Using statistics and social network analysis (SNA) on real data we calibrated the relations between our agents and generate realistic synthetic data sets that were verified against the domain and validated statistically against the original source.
We then used the simulation tools to model common fraud scenarios to ascertain exactly how effective are fraud techniques such as the simplest form of statistical threshold detection, which is perhaps the most common in use. The preliminary results show that threshold detection is effective enough at keeping fraud losses at a set level. This means that there seems to be little economic room for improved fraud detection techniques.
We also implemented other applications for the simulator tools such as the set up of a triage model and the measure of cost of fraud. This showed to be an important help for managers that aim to prioritise the fraud detection and want to know how much they should invest in fraud to keep the loses below a desired limit according to different experimented and expected scenarios of fraud.
This thesis is based on the work presented in the following six papers.
The papers I, III, IV and V are published in peer-reviewed conference proceedings. Paper II is published in a journal and Paper VI is ongoing work and will be submitted to a selected journal.
E. A. Lopez-Rojas, S. Axelsson, and D. Gorton. RetSim: A Shoe Store Agent-Based Simulation for Fraud Detection. In: The 25th European Modeling and Simulation Symposium (2013). (Best Paper Award)
E. Lopez-Rojas, D. Gorton and S. Axelsson. Using the RetSim simulator for fraud detection research. In: International Journal of Simulation and Process Modelling 10.2 (2015), p. 144
E. A. Lopez-Rojas and S. Axelsson. Social Simulation of Commercial and Financial Behaviour for Fraud Detection Research. In: Advances in Computational Social Science and Social Simulation. Barcelona, 2014
E. A. Lopez-Rojas. Extending the RetSim Simulator for Estimating the Cost of fraud in the Retail Store Domain. eng. In: The 27th European Modeling and Simulation Symposium-EMSS, Bergeggi, Italy. 2015
E. A. Lopez-Rojas and S. Axelsson. Using the RetSim Fraud Simulation Tool to set Thresholds for Triage of Retail Fraud. In: 20th Nordic Conference on Secure IT Systems, NordSec 2015. Stockholm: Springer, 2015, pp. 156-171
E. Lopez-Rojas and S. Axelsson. Applications of the PaySim simulator for fraud detection research. In: TBD (2016).
There are other papers that are not included in this thesis but are related to this research:
E. A. Lopez-Rojas and S. Axelsson. Money Laundering Detection using Synthetic Data. In: The 27th workshop of Swedish Artificial Intelligence Society (SAIS) (2012), pp. 33-40
E. A. Lopez-Rojas and S. Axelsson. Multi Agent Based Simulation (MABS) of Financial Transactions for Anti Money Laundering (AML). in: The 17th Nordic Conference on Secure IT Systems (2012), pp. 25-32
E. A. Lopez-Rojas and S. Axelsson. Banksim: A bank payments simulator for fraud detection research. In: 26th European Modeling and Simulation Symposium, EMSS 2014. Dime University of Genoa, 2014, pp. 144-152
E. A. Lopez-Rojas, and S. Axelsson. A Review of Computer Simulation for Fraud Detection Research in Financial Datasets. In: Future Technologies Conference, San Francisco, USA. 2016 (accepted)
E. A. Lopez-Rojas , A. Elmir, and S. Axelsson. PaySim: A financial mobile money simulator for fraud detection. In: The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus. 2016 (accepted)