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Social Simulation of Commercial and Financial Behaviour for Fraud Detection Research
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-9158-3488
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2014 (English)In: Advances in Computational Social Science and Social Simulation / [ed] Miguel, Amblard, Barceló & Madella, Barcelona, 2014Conference paper (Refereed)
Abstract [en]

We present a social simulation model that covers three main financialservices: Banks, Retail Stores, and Payments systems. Our aim is toaddress the problem of a lack of public data sets for fraud detectionresearch in each of these domains, and provide a variety of fraudscenarios such as money laundering, sales fraud (based on refunds anddiscounts), and credit card fraud. Currently, there is a general lackof public research concerning fraud detection in the financial domainsin general and these three in particular. One reason for this is thesecrecy and sensitivity of the customers data that is needed toperform research. We present PaySim, RetSim, and BankSim asthree case studies of social simulations for financial transactionsusing agent-based modelling. These simulators enable us to generatesynthetic transaction data of normal behaviour of customers, and alsoknown fraudulent behaviour. This synthetic data can be used to furtheradvance fraud detection research, without leaking sensitiveinformation about the underlying data. Using statistics and socialnetwork analysis (SNA) on real data we can calibrate the relationsbetween staff and customers, and generate realistic synthetic datasets. The generated data represents real world scenarios that arefound in the original data with the added benefit that this data canbe shared with other researchers for testing similar detection methodswithout concerns for privacy and other restrictions present when usingthe original data.

Place, publisher, year, edition, pages
Barcelona, 2014.
Keyword [en]
Privacy; Anonymization; Multi-Agent-Based Simulation; MABS; ABS; Retail Store; Fraud Detection; Synthetic Data
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:bth-12931OAI: oai:DiVA.org:bth-12931DiVA: diva2:954146
Conference
Social Simulation Conference. Bellaterra, Cerdanyola del Valles, 1a : 2014
Available from: 2016-08-20 Created: 2016-08-20 Last updated: 2016-08-30Bibliographically approved
In thesis
1. Applying Simulation to the Problem of Detecting Financial Fraud
Open this publication in new window or tab >>Applying Simulation to the Problem of Detecting Financial Fraud
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2016
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 6
Keyword
security, privacy, anonymisation, multi-agent-based simulation, MABS, ABS, retail store, fraud detection, synthetic data, mobile money
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-12932 (URN)978-91-7295-329-1 (ISBN)
External cooperation:
Public defence
2016-10-28, J1650, Blekinge Institute of Technology, Karlskrona, 10:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation, 20140032
Available from: 2016-08-30 Created: 2016-08-20 Last updated: 2016-09-01Bibliographically approved

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