Fraud Audit Based on Visual Analysis: A Process Mining Approach
Source abstract
Abstract
Among the knowledge areas in which process mining has had an impact, the audit domain is particularly striking. Traditionally, audits seek evidence in a data sample that allows making inferences about a population. Mistakes are usually committed when generalizing the results and anomalies; therefore, they appear in unprocessed sets; however, there are some efforts to address these limitations using process-mining-based approaches for fraud detection. To the best of our knowledge, no fraud audit method exists that combines process mining techniques and visual analytics to identify relevant patterns. This paper presents a fraud audit approach based on the combination of process mining techniques and visual analytics. The main advantages are: (i) a method is included that guides the use of the visual capabilities of process mining to detect fraud data patterns during an audit; (ii) the approach can be generalized to any business domain; (iii) well-known process mining techniques are used (dotted chart, trace alignment, fuzzy miner…). The techniques were selected by a group of experts and were extended to enable filtering for contextual analysis, to handle levels of process abstraction, and to facilitate implementation in the area of fraud audits. Based on the proposed approach, we developed a software solution that is currently being used in the financial sector as well as in the telecommunications and hospitality sectors. Finally, for demonstration purposes, we present a real hotel management use case in which we detected suspected fraud behaviors, thus validating the effectiveness of the approach.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Business Process Modeling and Analysis · Semantic Web and Ontologies · Big Data and Business Intelligence
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
CENATAV, 7ma A#21406, Rpto. Siboney Playa, 74390 La Havana, Cuba
Lucentia Research Group, Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
DATYS, 5 Street N#3401, Rpto. Miramar Playa, 11300 La Havana, Cuba
Funding
Ministerio de Ciencia, Innovación y Universidades
References (89)
Show source-provided references
1. Association of Certified Fraud Examiners (2018). Report to the Nations: 2018 Global Study on Occupational Fraud and Abuse, ACEF.
2. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature10.1016/j.dss.2010.08.006 ↗
3. Auditing 2.0: Using Process Mining to Support Tomorrow’s Auditor10.1109/MC.2010.61 ↗
4. Werner, M., Gehrke, N., and Nuttgens, M. (2012, January 4–7). Business Process Mining and Reconstruction for Financial Audits. Proceedings of the 45th Hawaii International Conference on System Sciences, Maui, HI, USA.10.1109/HICSS.2012.141 ↗
5. Accorsi, R., and Wonnemann, C. (2010, January 3–5). Auditing Workflow Executions against Dataflow Policies. Proceedings of the Business Information Systems, 13th International Conference, BIS 2010, Berlin, Germany.10.1007/978-3-642-12814-1_18 ↗
6. Vona, L.W. (2008). Fraud Risk Assessment: Building a Fraud Audit Program, J. Wiley & Sons.
7. Byrnes, P.E., Criste, T.R., Stewart, T.R., and Vasarhelyi, M. (2015). Reimagining Auditing in a Wired World. Audit Analytics and Continuous Audit: Looking toward the Future, American Institute of Certified Public Accountants (AICPA).
8. Time Is Ripe to Revolutionize the Audit10.1080/07366981.2017.1380479 ↗
9. Big Data and Analytics in the Modern Audit Engagement: Research Needs10.2308/ajpt-51684 ↗
10. Big data techniques in auditing research and practice: Current trends and future opportunities10.1016/j.acclit.2017.05.003 ↗
11. Understanding usage and value of audit analytics for internal auditors: An organizational approach10.1016/j.accinf.2017.12.005 ↗
12. Analytical procedures in external auditing: A comprehensive literature survey and framework for external audit analytics10.1016/j.acclit.2018.01.001 ↗
13. Vona, L.W. (2017). Fraud Data Analytics Methodology: The Fraud Scenario Approach to Uncovering Fraud in Core Business Systems, John Wiley & Sons.10.1002/9781119270331 ↗
14. Claes, J., and Poels, G. (2012, January 3). Process Mining and the ProM Framework: An Exploratory Survey. Proceedings of the Business Process Management Workshops—BPM 2012 International Workshops, Tallinn, Estonia. Revised Papers.10.1007/978-3-642-36285-9_19 ↗
15. One Picture Is Worth at Least a Million Neurons10.1016/j.cub.2004.05.045 ↗
16. Iliinsky, N., and Steele, J. (2011). Designing Data Visualizations: Representing Informational Relationships, O’Reilly Media.
17. Superior loan collectibility judgments given graphical displays
18. An empirical investigation of the effects of presentation format and personality on auditor’s judgment in applying analytical procedures
19. The Effects Of Experience And Data Presentation Format On An Auditing Judgment
20. Alawadhi, A. (2015). The Application of Data Visualization in Auditing. [Ph.D. Thesis, Rutgers University-Graduate School-Newark].
21. Visual analytics for event detection: Focusing on fraud
22. Shi, Y., Liu, Y., Tong, H., He, J., Yan, G., and Cao, N. (2020). Visual Analytics of Anomalous User Behaviors: A Survey. IEEE Trans. Big Data, 1.10.1109/TBDATA.2020.2964169 ↗
23. EVA: Visual Analytics to Identify Fraudulent Events10.1109/TVCG.2017.2744758 ↗
24. Fischer, F., and Keim, D.A. (2014, January 10). NStreamAware: Real-time visual analytics for data streams to enhance situational awareness. Proceedings of the Eleventh Workshop on Visualization for Cyber Security, Paris, France.10.1145/2671491.2671495 ↗
25. Suntinger, M., Obweger, H., Schiefer, J., and Gröller, M.E. (2008, January 5–7). The Event Tunnel: Interactive Visualization of Complex Event Streams for Business Process Pattern Analysis. Proceedings of the IEEE VGTC Pacific Visualization Symposium 2008, PacificVis 2008, Kyoto, Japan.10.1109/PACIFICVIS.2008.4475466 ↗
26. Legg, P.A. (2015, January 25). Visualizing the insider threat: Challenges and tools for identifying malicious user activity. Proceedings of the 2015 IEEE Symposium on Visualization for Cyber Security, VizSec 2015, Chicago, IL, USA.10.1109/VIZSEC.2015.7312772 ↗
27. Gómez, J.A.G., Wilson, A., Liu, J., Davies, D., Jarvis, P., and Bier, E. (2016, January 7–10). Network Explorer: Design, Implementation, and Real World Deployment of a Large Network Visualization Tool. Proceedings of the International Working Conference on Advanced Visual Interfaces, AVI 2016, Bari, Italy.
28. Niu, Z., Cheng, D., Zhang, L., and Zhang, J. (2018, January 10–13). Visual Analytics for Networked-Guarantee Loans Risk Management. Proceedings of the IEEE Pacific Visualization Symposium, PacificVis 2018, Kobe, Japan.10.1109/PacificVis.2018.00028 ↗
29. Didimo, W., Liotta, G., Montecchiani, F., and Palladino, P. (2011, January 1–4). An advanced network visualization system for financial crime detection. Proceedings of the IEEE Pacific Visualization Symposium, PacificVis 2011, Hong Kong, China.10.1109/PACIFICVIS.2011.5742391 ↗
30. Argyriou, E.N., Symvonis, A., and Vassiliou, V. (2014, January 5–8). A Fraud Detection Visualization System Utilizing Radial Drawings and Heat-maps. Proceedings of the 5th International Conference on Information Visualization Theory and Applications, IVAPP 2014, Lisbon, Portugal.
31. Schäfer, M., Wanner, F., Mansmann, F., Scheible, C., Stennett, V., Hasselrot, A.T., and Keim, D.A. (2011, January 24–25). Visual pattern discovery in timed event data. Proceedings of the Visualization and Data Analysis 2011, San Francisco, CA, USA.10.1117/12.871870 ↗
32. Sun, J., Zhu, Q., Liu, Z., Liu, X., Lee, J., Su, Z., Shi, L., Huang, L., and Xu, W. (2018, January 10–13). FraudVis: Understanding Unsupervised Fraud Detection Algorithms. Proceedings of the IEEE Pacific Visualization Symposium, PacificVis 2018, Kobe, Japan.10.1109/PacificVis.2018.00029 ↗
33. Data visualization for fraud detection: Practice implications and a call for future research10.1016/j.accinf.2015.01.001 ↗
34. van der Aalst, W.M.P., Adriansyah, A., de Medeiros, A.K.A., Arcieri, F., Baier, T., Blickle, T., Bose, R.P.J.C., van den Brand, P., Brandtjen, R., and Buijs, J.C.A.M. (2011, January 29). Process Mining Manifesto. Proceedings of the Business Process Management Workshops—BPM 2011 International Workshops, Clermont-Ferrand, France.
35. Sequence partitioning for process mining with unlabeled event logs10.1016/j.datak.2011.05.003 ↗
36. Process mining: A research agenda10.1016/j.compind.2003.10.001 ↗
37. Conformance checking of processes based on monitoring real behavior10.1016/j.is.2007.07.001 ↗
38. Munoz-Gama, J., and Carmona, J. (2010, January 13–16). A Fresh Look at Precision in Process Conformance. Proceedings of the Business Process Management - 8th International Conference, BPM 2010, Hoboken, NJ, USA.
39. Hosseinpour, M., and Jans, M. (2016, January 15–16). Categorizing Identified Deviations for Auditing. Proceedings of the 6th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2016), Graz, Austria.
40. Discovering simulation models10.1016/j.is.2008.09.002 ↗
41. Jans, M., Alles, M., and Vasarhelyi, M. (2010). Process Mining of Event Logs in Auditing: Opportunities and Challenges. Ssrn Electron. J.10.2139/ssrn.2488737 ↗
42. van der Aalst, W.M.P. (2016). Process Mining—Data Science in Action, Springer. [2nd ed.].10.1007/978-3-662-49851-4 ↗
43. Gschwandtner, T. (2015, January 9–11). Visual Analytics Meets Process Mining: Challenges and Opportunities. Proceedings of the Data-Driven Process Discovery and Analysis—5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria.
44. Kriglstein, S., Pohl, M., Rinderle-Ma, S., and Stallinger, M. (2016, January 6–7). Visual Analytics in Process Mining: Classification of Process Mining Techniques. Proceedings of the EuroVis Workshop on Visual Analytics, EuroVA 2016, Groningen, The Netherlands.
45. Dixit, P.M., Caballero, H.S.G., Corvò, A., Hompes, B.F.A., Buijs, J.C.A.M., and van der Aalst, W.M.P. (2017, January 21–23). Enabling Interactive Process Analysis with Process Mining and Visual Analytics. Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), Porto, Portugal.
46. Hompes, B., Buijs, J.C.A.M., and van der Aalst, W.M.P. (2016, January 24–28). A Generic Framework for Context-Aware Process Performance Analysis. Proceedings of the On the Move to Meaningful Internet Systems: OTM 2016 Conferences—Confederated International Conferences: CoopIS, C&TC, and ODBASE 2016, Rhodes, Greece.10.1007/978-3-319-48472-3_17 ↗
47. Conceptual model for online auditing10.1016/j.dss.2010.08.014 ↗
48. A Field Study on the Use of Process Mining of Event Logs as an Analytical Procedure in Auditing10.2308/accr-50807 ↗
49. Jans, M., Depaire, B.t., and Vanhoof, K. (2011, January 20–21). Does Process Mining Add to Internal Auditing? An Experience Report. Proceedings of the Enterprise, Business-Process and Information Systems Modeling—12th International Conference, BPMDS 2011, and 16th International Conference, EMMSAD 2011, held at CAiSE 2011, London, UK.
50. van der Aalst, W.M.P., de Beer, H.T., and van Dongen, B.F. (November, January 31). Process Mining and Verification of Properties: An Approach Based on Temporal Logic. Proceedings of the On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE, OTM Confederated International Conferences CoopIS, DOA, and ODBASE 2005, Agia Napa, Cyprus.10.1007/11575771_11 ↗
51. The LTL Checker Plugins: A Reference Manual
52. Rozinat, A. (2010). Process Mining: Conformance and Extension. [Ph.D. Thesis, Technische Universiteit Eindhoven].
53. de Lima Bezerra, F., Wainer, J., and van der Aalst, W.M.P. (2009, January 8–9). Anomaly Detection Using Process Mining. Proceedings of the Enterprise, Business-Process and Information Systems Modeling, 10th International Workshop, BPMDS 2009, and 14th International Conference, EMMSAD 2009, held at CAiSE 2009, Amsterdam, The Netherlands.
54. Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance10.1016/j.entcs.2004.10.013 ↗
55. Bezerra, F., and Wainer, J. (2008, January 24–27). Auditing workflow logs for fraud detection. Proceedings of the KDD 2008 Workshop on Data Mining for Business Applications, Las Vegas, NV, USA.
56. Chiu, T., Wang, Y., and Vasarhelyi, M. (2017). A Framework of Applying Process Mining for Fraud Scheme Detection. Ssrn Electron. J.10.2139/ssrn.2995286 ↗
57. Chiu, T., Vasarhelyi, M., Alrefai, A., and Yan, Z. (2018). Validating Process Mining: A Framework Integrating Auditorrs Risk Assessment. Ssrn Electron. J.10.2139/ssrn.3136007 ↗
58. Process Mining of Event Logs: A Case Study Evaluating Internal Control Effectiveness10.2308/acch-52458 ↗
59. Process-mining-enabled audit of information systems: Methodology and an application10.1016/j.eswa.2018.05.030 ↗
60. Reducing false positives in fraud detection: Combining the red flag approach with process mining10.1016/j.accinf.2018.03.004 ↗
61. Multilevel Process Mining for Financial Audits10.1109/TSC.2015.2457907 ↗
62. Gehrke, N. (2010, January 12–15). Basic Principles of Financial Process Mining A Journey through Financial Data in Accounting Information Systems. Proceedings of the Sustainable IT Collaboration Around the Globe, 16th Americas Conference on Information Systems, AMCIS 2010, Lima, Peru.
63. Werner, M. (2013, January 11–13). Colored Petri Nets for Integrating the Data Perspective in Process Audits. Proceedings of the Conceptual Modeling—32th International Conference, ER 2013, Hong Kong, China.
64. Werner, M., Schultz, M., Müller-Wickop, N., Gehrke, N., and Nüttgens, M. (2012, January 16–19). Tackling Complexity: Process Reconstruction and Graph Transformation for Financial Audits. Proceedings of the International Conference on Information Systems, ICIS 2012, Orlando, FL, USA.
65. Werner, M., and Nüttgens, M. (2014, January 6–9). Improving Structure: Logical Sequencing of Mined Process Models. Proceedings of the 47th Hawaii International Conference on System Sciences, HICSS 2014, Waikoloa, HI, USA.10.1109/HICSS.2014.482 ↗
66. Financial process mining—Accounting data structure dependent control flow inference10.1016/j.accinf.2017.03.004 ↗
67. Werner, M. (2019, January 8–11). Materiality Maps—Process Mining Data Visualization for Financial Audits. Proceedings of the 52nd Hawaii International Conference on System Sciences, HICSS 2019, Maui, HI, USA.10.24251/HICSS.2019.129 ↗
68. Vona, L.W. (2011). The Fraud Audit: Responding to the Risk of Fraud in Core Business Systems, John Wiley & Sons.10.1002/9781118387047 ↗
69. XES Working Group (2016). IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams. IEEE Std 1849–2016, 1–50.
70. IEEE 1849: The XES Standard: The Second IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs]10.1109/MCI.2017.2670420 ↗
71. A Systematic Literature Review of Fraud Detection Metrics in Business Processes10.1109/ACCESS.2020.2971604 ↗
72. The Delphi Method for Graduate Research
73. Dalkey, N., Brown, B., and Cochran, S. (1969). The Delphi Method, III: Use of Self-Ratings to Improve Group Estimates, RAND CORPL.10.1016/0099-3964(70)90029-3 ↗
74. La utilización del juicio de experto para la evaluación de TIC: El Coeficiente de competencia experta10.13042/brp.2013.65202 ↗
75. van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., and van der Aalst, W.M.P. (2005, January 20–25). The ProM Framework: A New Era in Process Mining Tool Support. Proceedings of the Applications and Theory of Petri Nets 2005, 26th International Conference, ICATPN 2005, Miami, FL, USA.10.1007/11494744_25 ↗
76. Santos, A.C. (2006). Tecnología de Gestión de Recursos Humanos, Editorial Félix Varela.
77. Siegel, S., and Castellan, N.J. (1988). Nonparametric Statistics for the Behavioral Sciences, McGraw–Hill, Inc.
78. Managing Delphi Surveys Using Nonparametric Statistical Techniques*10.1111/j.1540-5915.1997.tb01330.x ↗
79. Minitab, I. (2021, May 19). MINITAB 15 Statistical Software, 15. Available online: https://www.minitab.com.
80. What is the Analytic Hierarchy Process?10.1007/978-3-642-83555-1_5 ↗
81. Günther, C.W., and van der Aalst, W.M.P. (2007, January 24–28). Fuzzy Mining—Adaptive Process Simplification Based on Multi-perspective Metrics. Proceedings of the Business Process Management, 5th International Conference, BPM 2007, Brisbane, Australia.10.1007/978-3-540-75183-0_24 ↗
82. Song, M., and Aalst, W. (2007, January 8–9). Supporting Process Mining by Showing Events at a Glance. Proceedings of the WITS 2007—Proceedings, 17th Annual Workshop on Information Technologies and Systems, Montreal, QC, Canada.
83. van der Aalst, W.M.P. (2011). Process Mining—Discovery, Conformance and Enhancement of Business Processes, Springer.10.1007/978-3-642-19345-3 ↗
84. Bose, R.P.J.C., and van der Aalst, W.M.P. (2012). Process diagnostics using trace alignment: Opportunities, issues, and challenges. Inf. Syst., 117–141.10.1016/j.is.2011.08.003 ↗
85. Discovering Social Networks from Event Logs10.1007/s10606-005-9005-9 ↗
86. Campigotto, R., Céspedes, P.C., and Guillaume, J.-L. (2014). A Generalized and Adaptive Method for Community Detection. arXiv.
87. Prado-Romero, M.A., Doerr, C., and Alonso, A.G. (2017, January 7–10). Discovering Bitcoin Mixing Using Anomaly Detection. Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications—22nd Iberoamerican Congress, CIARP 2017, Valparaíso, Chile.
88. Jans, M., Alles, M., and Vasarhelyi, M. (2011, January 18–19). Process mining of event logs in internal auditing: A case study. Proceedings of the The 2nd International Symposium on Accounting Information Systems, Roma, Italy.
89. Hornix, P.T.G. (2007). Performance Analysis of Business Processes through Process Mining. [Master’s Thesis, Eindhoven University of Technology].