FraudFind: Financial fraud detection by analyzing human behavior

Financial fraud is commonly represented by the use of illegal practices where they can intervene from senior managers until payroll employees, becoming a crime punishable by law. There are many techniques developed to analyze, detect and prevent this behavior, being the most important the fraud triangle theory associated with the classic financial audit model. In order to perform this research, a survey of the related works in the existing literature was carried out, with the purpose of establishing our own framework. In this context, this paper presents FraudFind, a conceptual framework that allows to identify and outline a group of people inside an banking organization who commit fraud, supported by the fraud triangle theory. FraudFind works in the approach of continuous audit that will be in charge of collecting information of agents installed in user's equipment. It is based on semantic techniques applied through the collection of phrases typed by the users under study for later being transferred to a repository for later analysis. This proposal encourages to contribute with the field of cybersecurity, in the reduction of cases of financial fraud.

[1]  N. Omar,et al.  Fraud diamond risk indicator: An assessment of its importance and usage , 2010, 2010 International Conference on Science and Social Research (CSSR 2010).

[2]  Misha Kakkar,et al.  Geo-identification of web users through logs using ELK stack , 2016, 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence).

[3]  Valeriu Manuel Ionescu.  The analysis of the performance of RabbitMQ and ActiveMQ , 2015, 2015 14th RoEduNet International Conference - Networking in Education and Research (RoEduNet NER).

[4]  Prabin Kumar Panigrahi.  A Framework for Discovering Internal Financial Fraud Using Analytics , 2011, 2011 International Conference on Communication Systems and Network Technologies.

[5]  Dhiya Al-Jumeily,et al.  The Development of Fraud Detection Systems for Detection of Potentially Fraudulent Applications , 2015, 2015 International Conference on Developments of E-Systems Engineering (DeSE).

[6]  Randall F. Trzeciak,et al.  Common Sense Guide to Prevention and Detection of Insider Threats , 2006 .

[7]  Mohiuddin Ahmed,et al.  A novel approach for outlier detection and clustering improvement , 2013, 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA).

[8]  Jen Mailley,et al.  A tale of two triangles: comparing the Fraud Triangle with criminology’s Crime Triangle , 2015 .

[9]  Dong Nguyen Doan,et al.  Tuning Logstash Garbage Collection for High Throughput in a Monitoring Platform , 2016, 2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC).

[10]  Carolyn F. Holton,et al.  Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem , 2009, Decis. Support Syst..

[11]  Yong-Yi Wang,et al.  Design and Implementation of an Indexing Method Based on Fields for Elasticsearch , 2015, 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC).

[12]  Siddharth S. Rautaray,et al.  Performance of elasticsearch in cloud environment with nGram and non-nGram indexing , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[13]  Subramanyeswara Rao Dasari,et al.  Big Spectrum Data Analysis in DSA Enabled LTE-A Networks: A System Architecture , 2016, 2016 IEEE 6th International Conference on Advanced Computing (IACC).

[14]  Koen Vanhoof,et al.  Internal Fraud Risk Reduction - Results of a Data Mining Case Study , 2010, ICEIS.

[15]  Dimitris Gritzalis,et al.  An Insider Threat Prediction Model , 2010, TrustBus.

[16]  Michael H. Breitner,et al.  Fraud Prediction and the Human Factor: An Approach to Include Human Behavior in an Automated Fraud Audit , 2012, 2012 45th Hawaii International Conference on System Sciences.

[17]  Haibing Li,et al.  Financial fraud detection by using Grammar-based multi-objective genetic programming with ensemble learning , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[18]  H. Raghav Rao,et al.  A solution architecture for financial institutions to handle illegal activities: a neural networks approach , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[19]  Chao-Hsien Chu,et al.  A Review of Data Mining-Based Financial Fraud Detection Research , 2007, 2007 International Conference on Wireless Communications, Networking and Mobile Computing.