Detecting Financial Statement Irregularities: Hybrid Benford-Outlier-Process-Mining Anomaly Detection Architecture

References

[1] K. Singh, P. J. Best, M. Bojilov, and C. Blunt, “Continuous Auditing and Continuous Monitoring in ERP Environments: Case Studies of Application Implementations,” Journal of Information Systems, vol. 28, no. 1, pp. 287–310, Jun. 2014, 50679.

[2] J. Dai and M. A. Vasarhelyi, “Toward Blockchain-Based Accounting and Assurance,” Journal of Information Systems, vol. 31, no. 3, pp. 5–21, Sep. 2017, 10.2308/ISYS-51804.

[3] R. Narula and J. H. Dunning, “Industrial development, globalization and multinational enterprises: New realities for developing countries,” Oxford Development Studies, vol. 28, no. 2, pp. 141–167, 2000, 10.1080/713688313.

[4] K. E. Meyer and M. W. Peng, “Probing theoretically into Central and Eastern Europe: Transactions, resources, and institutions,” J Int Bus Stud, vol. 36, no. 6, pp. 600–621, 2005,

[5] R. Kaplinsky, “Globalisation and unequalisation: What can be learned from value chain analysis?,” Journal of Development Studies, vol. 37, no. 2, pp. 117– 146, 2000,

[6] B. Jindra, A. Giroud, and J. Scott-Kennel, “Subsidiary roles, vertical linkages and economic development: Lessons from transition economies,” Journal of World Business, vol. 44, no. 2, pp. 167–179, Apr. 2009,

[7] S. Young and A. T. Tavares, “Centralization and autonomy: Back to the future,” International Business Review, vol. 13, no. 2, pp. 215 –237, 2004, 10.1016/J.IBUSREV.2003.06.002.

[8] R. Narula, “Innovation systems and ‘inertia’ in R&D location: Norwegian firms and the role of systemic lock-in,” Res Policy, vol. 31, no. 5, pp. 795–816, Jul. 2002, 7333(01)00148-2.

[9] H. W. C. Yeung, W. Liu, and P. Dicken, “Transnational corporations and network effects of a local manufacturing cluster in mobile telecommunications equipment in China,” World Dev, vol. 34, no. 3, pp. 520–540, Mar. 2006, 10.1016/J.WORLDDEV.2005.07.019.

[10] D. Rodrik, A. Subramanian, and F. Trebbi, “Institutions rule: The primacy of institutions over geography and integration in economic development,” Journal of Economic Growth, vol. 9, no. 2, pp. 131–165, Jun. 2004, 10.1023/B:JOEG.0000031425.72248.85.

[11] C. Wang and L. Yu, “Do spillover benefits grow with rising foreign direct investment? An empirical examination of the case of China,” Appl Econ, vol. 39, no. 3, pp. 397–405, Feb. 2007,

[12] K. E. Meyer, M. Wright, and S. Pruthi, “Research notes and commentaries managing knowledge in foreign entry strategies: A resource-based analysis,” Strategic Management Journal, vol. 30, no. 5, pp. 557– 574, May 2009,

[13] A. Mody, “Is FDI integrating the world economy?,” World Economy, vol. 27, no. 8, pp. 1195–1222, Aug. 2004, 9701.2004.00647.X.

[14] A. Marin and M. Bell, “Technology spillovers from Foreign Direct Investment (FDI): The active role of MNC subsidiaries in Argentina in the 1990s,” Journal of Development Studies, vol. 42, no. 4, pp. 678–697, May 2006, 10.1080/00220380600682298.

[15] M. Riani, A. C. Atkinson, and D. Perrotta, “A parametric framework for the comparison of methods of very robust regression,” Statistical Science, vol. 29, no. 1, pp. 128–143, 2014, 10.1214/13-STS437.

[16] P. Criscuolo, R. Narula, and B. Verspagen, “Role of home and host country innovation systems in r&d internationalisation: A patent citation analysis,” Economics of Innovation and New Technology, vol. 14, no. 5, pp. 417– 433, 2005, 10.1080/1043859042000315285.

[17] J. H. Dunning and R. Narula, “Multinationals and industrial competitiveness: A new agenda,” Multinationals and Industrial Competitiveness: A New Agenda, pp. 1–287, Jan. 2004,

[18] “Full article: On Characterizations and Tests of Benford’s Law.” Accessed: Sep. 18, 2018. [Online]. Available: https://www.tandfonline.com/ 01621459.2019.1891927

[19] W. K. T. Cho and B. J. Gaines, “Breaking the (Benford) law: Statistical fraud detection in campaign finance,” American Statistician, vol. 61, no. 3, pp. 218–223, Aug. 2007, 10.1198/000313007X223496.

[20] R. J. Serfling, “Approximation Theorems of Mathematical Statistics,” Approximation Theorems of Mathematical Statistics, pp. 1– 371, Jan. 2008,

[21] P. Rubin-Delanchy, N. A. Heard, and D. J. Lawson, “Meta-Analysis of Mid-p-Values: Some New Results based on the Convex Order,” J Am Stat Assoc, vol. 114, no. 527, pp. 1105–1112, Jul. 2019, 10.1080/01621459.2018.1469994.

[22] P. Rousseeuw, D. Perrotta, M. Riani, and M. Hubert, “Robust Monitoring of Time Series with Application to Fraud Detection,” Econom Stat, vol. 9, pp. 108–121, Jan. 2019, 10.1016/J.ECOSTA.2018.05.001.

[23] R. M. Fewster, “A simple explanation of benford’s law,” American Statistician, vol. 63, no. 1, pp. 26 –32, 2009, 10.1198/TAST.2009.0005.

[24] L. Pietronero, E. Tosatti, V. Tosatti, and A. Vespignani, “Explaining the uneven distribution of numbers in nature: The laws of Benford and Zipf,” Physica A: Statistical Mechanics and its Applications, vol. 293, no. 1–2, pp. 297–304, Apr. 2001, 10.1016/S0378-4371(00)00633-6.

[25] M. Riani, A. Corbellini, and A. C. Atkinson, “The Use of Prior Information in Very Robust Regression for Fraud Detection,” International Statistical Review, vol. 86, no. 2, pp. 205–218, Aug. 2018,

[26] M. J. Nigrini, “Benford’s law: Applications for forensic accounting, auditing, and fraud detection,” Benford’s Law: Applications for Forensic Accounting, Auditing, and Fraud Detection, pp. 1–330, Jan. 2012, 10.1002/9781119203094.

[27] L. Pericchi and D. Torres, “Quick anomaly detection by the Newcomb-Benford Law, with applications to electoral processes data from the USA, Puerto Rico and Venezuela,” Statistical Science, vol. 26, no. 4, pp. 502–516, Nov. 2011,

[28] S. J. Miller and M. J. Nigrini, “Order statistics and benford’s law,” Int J Math Math Sci, vol. 2008, 2008,

[29] “Benford’s Law,” Benford’s Law, Jul. 2015,

[30] A. Cerioli and D. Perrotta, “Robust clustering around regression lines with high density regions,” Adv Data Anal Classif, vol. 8, no. 1, pp. 5–26, 2014, 0151-5.

[31] A. Cerioli, L. Barabesi, A. Cerasa, M. Menegatti, and D. Perrotta, “Newcomb– Benford law and the detection of frauds in international trade,” Proc Natl Acad Sci U S A, vol. 116, no. 1, pp. 106–115, Jan. 2019, 10.1073/PNAS.1806617115.

[32] J. Hemerly, “Public policy considerations for data-driven innovation,” Computer (Long Beach Calif), vol. 46, no. 6, pp. 25–31, 2013,

[33] J. N. K. SL Brunton, “Data-driven versus physics-based modeling,” Annu Rev Fluid Mech, vol. 50, pp. 645–668, 2018.

[34] S. Fosso Wamba, S. Akter, A. Edwards, G. Chopin, and D. Gnanzou, “How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study,” Int J Prod Econ, vol. 165, pp. 234–246, Jul. 2015,

[35] P. Goodall, R. Sharpe, and A. West, “A data- driven simulation to support remanufacturing operations,” Comput Ind, vol. 105, pp. 48–60, Feb. 2019, 10.1016/J.COMPIND.2018.11.001.

[36] M. A. Waller and S. E. Fawcett, “Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management,” Journal of Business Logistics, vol. 34, no. 2, pp. 77–84, 2013, 10.1111/JBL.12010.

[37] P. Kumar Jain, K. Mandli, I. Hoteit, O. Knio, and C. Dawson, “Dynamically adaptive data- driven simulation of extreme hydrological flows,” Ocean Model (Oxf), vol. 122, pp. 85– 103, Feb. 2018, 10.1016/J.OCEMOD.2017.12.004.

[38] S. C. Y. L. X Hu, “Optimization of FMCG supply chain by using data-driven methods,” J Intell Manuf, vol. 30, no. 1, pp. 81–92, 2019.

[39] X. L. J. L. J Guo, “Physics-based and data- driven modeling: a comparative study,” Mathematics, vol. 7, no. 3, pp. 607–623, 2019.

[40] N. H. Wellalage and V. Fernandez, “Innovation and SME finance: Evidence from developing countries,” International Review of Financial Analysis, vol. 66, Nov. 2019, 10.1016/J.IRFA.2019.06.009.

[41] J. Gupta and A. Gregoriou, “Impact of market- based finance on SMEs failure,” Econ Model, vol. 69, pp. 13–25, Jan. 2018, 10.1016/J.ECONMOD.2017.09.004.

[42] R. Kersten, J. Harms, K. Liket, and K. Maas, “Small Firms, large Impact? A systematic review of the SME Finance Literature,” World Dev, vol. 97, pp. 330–348, Sep. 2017, 10.1016/J.WORLDDEV.2017.04.012.

[43] N. Kammoun, A. Bounfour, A. Özaygen, and R. Dieye, “Financial market reaction to cyberattacks,” Cogent Economics and Finance, vol. 7, no. 1, 2019, 10.1080/23322039.2019.1645584.

[44] Y. Asai, “Why do small and medium enterprises (SMEs) demand property liability insurance?,” J Bank Financ, vol. 106, pp. 298– 304, Sep. 2019, 10.1016/J.JBANKFIN.2019.07.012.

[45] I. Hasan, K. Jackowicz, O. Kowalewski, and Ł. Kozłowski, “Do local banking market structures matter for SME financing and performance? New evidence from an emerging economy,” J Bank Financ, vol. 79, pp. 142– 158, Jun. 2017, 10.1016/J.JBANKFIN.2017.03.009.

[46] D. Hirshleifer, “Behavioral Finance,” Annual Review of Financial Economics, vol. 7, no. Volume 7, 2015, pp. 133–159, Dec. 2015, 10.1146/ANNUREV-FINANCIAL-092214- 043752/CITE/REFWORKS.

[47] P. C. Tetlock, “Information transmission in finance,” Annual Review of Financial Economics, vol. 6, pp. 365–384, Dec. 2014, 110613-034449.

[48] S. Mullainathan, J. Schwartzstein, and W. J. Congdon, “A reduced-form approach to behavioral public finance,” Annu Rev Econom, vol. 4, pp. 511–540, Jul. 2012, 10.1146/ANNUREV-ECONOMICS-111809- 125033.

[49] V. N. C. Mushinada and V. S. S. Veluri, “Investors overconfidence behaviour at Bombay Stock Exchange,” International Journal of Managerial Finance, vol. 14, no. 5, pp. 613–632, Sep. 2018, 05-2017-0093.

[50] D. E. O’Leary, “Configuring blockchain architectures for transaction information in blockchain consortiums: The case of accounting and supply chain systems,” Intelligent Systems in Accounting, Finance and Management, vol. 24, no. 4, pp. 138–147, Oct. 2017,

[51] A. Barbon, M. Di Maggio, F. Franzoni, and A. Landier, “Brokers and Order Flow Leakage: Evidence from Fire Sales,” Journal of Finance, vol. 74, no. 6, pp. 2707–2749, Dec. 2019, 10.1111/JOFI.12840.

[52] M. D. Gould, M. A. Porter, S. Williams, M. McDonald, D. J. Fenn, and S. D. Howison, “Limit order books,” Quant Finance, vol. 13, no. 11, pp. 1709–1742, 2013, 10.1080/14697688.2013.803148.

[53] N. E. Popescu, “Entrepreneurship and SMEs Innovation in Romania,” Procedia Economics and Finance, vol. 16, pp. 512–520, 2014, 10.1016/S2212-5671(14)00832-6.

[54] M. Sipa, I. Gorzeń-Mitka, and A. Skibiński, “Determinants of Competitiveness of Small Enterprises: Polish Perspective,” Procedia Economics and Finance, vol. 27, pp. 445–453, 2015,

[55] R. Verma and P. Verma, “Behavioral biases and retirement assets allocation of corporate pension plans,” Review of Behavioral Finance, vol. 10, no. 4, pp. 353–369, Oct. 2018, 10.1108/RBF-01-2017-0009.

[56] A. Cerioli, A. Farcomeni, and M. Riani, “Wild adaptive trimming for robust estimation and cluster analysis,” Scandinavian Journal of Statistics, vol. 46, no. 1, pp. 235–256, Mar. 2019,

[57] W. R. Mebane, “Comment on ‘benford’s law and the detection of election fraud,’” Political Analysis, vol. 19, no. 3, pp. 269–272, Sep. 2011,

[58] S. Lall and R. Narula, “Foreign direct investment and its role in economic development: Do we need a new agenda?,” European Journal of Development Research, vol. 16, no. 3, pp. 447–464, Sep. 2004, 10.1080/0957881042000266589.

[59] J. Henderson, P. Dicken, M. Hess, N. Coe, and H. Wai-Chung Yeung, “Global production networks and the analysis of economic development,” Rev Int Polit Econ, vol. 9, no. 3, pp. 436 –464, 2002, 10.1080/09692290210150842.

[60] A. Tiwari, C. J. Turner, and B. Majeed, “A review of business process mining: state‐of‐ the‐art and future trends,” Business Process Management Journal, vol. 14, no. 1, pp. 5–22, Feb. 2008,

[61] P. Diaconis, “The Distribution of Leading Digits and Uniform Distribution Mod 1,” The Annals of Probability, vol. 5, no. 1, Dec. 2007,

[62] R. J. Bolton and D. J. Hand, “Statistical fraud detection: A review,” Statistical Science, vol. 17, no. 3, pp. 235–255, Aug. 2002, 10.1214/SS/1042727940.

[63] L. Dümbgen and C. Leuenberger, “Explicit Error Bounds via Total Variation,” Benford’s Law, May 2015, 10.23943/PRINCETON/9780691147611.003. 0005.

[64] M. Hubert, P. J. Rousseeuw, and S. Van Aelst, “High-breakdown robust multivariate methods,” Statistical Science, vol. 23, no. 1, pp. 92–119, Feb. 2008, 10.1214/088342307000000087.

[65] L. Dümbgen and C. Leuenberger, “Explicit bounds for the approximation error in Benford’s law,” Electronic Communications in Probability, vol. 13, pp. 99–112, Jan. 2008,

[66] A. Berger and I. Twelves, “On the significands of uniform random variables,” J Appl Probab, vol. 55, no. 2, pp. 353–367, Jun. 2018, 10.1017/JPR.2018.23.

[67] B. Luque and L. Lacasa, “The first-digit frequencies of prime numbers and Riemann zeta zeros,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 465, no. 2107, pp. 2197–2216, Jul. 2009,

[68] L. Barabesi, A. Cerasa, D. Perrotta, and A. Cerioli, “Modeling international trade data with the Tweedie distribution for anti-fraud and policy support,” Eur J Oper Res, vol. 248, no. 3, pp. 1031–1043, Feb. 2016, 10.1016/J.EJOR.2015.08.042.

[69] L. Lacasa, “Newcomb–benford law helps customs officers to detect fraud in international trade,” Proc Natl Acad Sci U S A, vol. 116, no. 1, pp. 11–13, Jan. 2019, 10.1073/PNAS.1819470116.

[70] A. Berger and T. P. Hill, “An Introduction to Benford’s Law,” May 2015, 10.1515/9781400866588.

[71] A. Berger and T. P. Hill, “Benford’s Law Strikes Back: No Simple Explanation in Sight for Mathematical Gem,” Mathematical Intelligencer, vol. 33, no. 1, pp. 85–91, Mar. 2011,

[72] W. R. Mebane, “Fraud in the 2009 Presidential Election in Iran?,” CHANCE, vol. 23, no. 1, pp. 6–15, Jan. 2010, 10.1080/09332480.2010.10739785.

[73] A. Cerioli, “Multivariate outlier detection with high-breakdown estimators,” J Am Stat Assoc, vol. 105, no. 489, pp. 147–156, Mar. 2010, 10.1198/JASA.2009.TM09147.

[74] H. A. Engel and C. Leuenberger, “Benford’s law for exponential random variables,” Stat Probab Lett, vol. 63, no. 4, pp. 361–365, Jul. 2003,

[75] J. Fernández-Gracia and L. Lacasa, “Bipartisanship Breakdown, Functional Networks, and Forensic Analysis in Spanish 2015 and 2016 National Elections,” Complexity, vol. 2018, 2018, 10.1155/2018/9684749.

[76] L. Barabesi, A. Cerasa, A. Cerioli, and D. Perrotta, “Goodness-of-Fit Testing for the Newcomb-Benford Law With Application to the Detection of Customs Fraud,” Journal of Business and Economic Statistics, vol. 36, no. 2, pp. 346–358, Apr. 2018, 10.1080/07350015.2016.1172014.

[77] A. Cerasa, F. Torti, and D. Perrotta, “Heteroscedasticity, multiple populations and outliers in trade data,” Studies in Theoretical and Applied Statistics, Selected Papers of the Statistical Societies, pp. 43–50, 2016, 10.1007/978-3-319-44093-4_5.

[78] L. Barabesi, A. Cerasa, A. Cerioli, and D. Perrotta, “A new family of tempered distributions,” Electron J Stat, vol. 10, no. 2, pp. 3871–3893, 2016, EJS1214.

[79] P. M. Hartmann, M. Zaki, N. Feldmann, and A. Neely, “Capturing value from big data–a taxonomy of data-driven business models used by start-up firms,” International Journal of Operations & Production Management, vol. 36, no. 10, pp. 1382–1406, 2016, 10.1108/ijopm-02-2014-0098.

[80] S. Barocas and H. Nissenbaum, “Big data’s end run around procedural privacy protections,” Commun ACM, vol. 57, no. 11, pp. 31–33, Nov. 2014,

[81] J. W. Y Zhang, “Data-driven modeling and scientific computing,” Appl Mech Rev, vol. 68, no. 5, pp. 050801–051013, 2016.

[82] C. Meng, S. S. Nageshwaraniyer, A. Maghsoudi, Y. J. Son, and S. Dessureault, “Data-driven modeling and simulation framework for material handling systems in coal mines,” Comput Ind Eng, vol. 64, no. 3, pp. 766 –779, 2013, 10.1016/J.CIE.2012.12.017.

[83] B. D. Mittelstadt and L. Floridi, “The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts,” Sci Eng Ethics, vol. 22, no. 2, pp. 303–341, Apr. 2016, 10.1007/S11948-015-9652-2.

[84] P. Luo, H. Wang, and Z. Yang, “Investment and financing for SMEs with a partial guarantee and jump risk,” Eur J Oper Res, vol. 249, no. 3, pp. 1161–1168, Mar. 2016, 10.1016/J.EJOR.2015.09.032.

[85] N. Yoshino and F. Taghizadeh-Hesary, “Optimal credit guarantee ratio for small and medium-sized enterprises’ financing: Evidence from Asia,” Econ Anal Policy, vol. 62, pp. 342– 356, Jun. 2019, 10.1016/J.EAP.2018.09.011.

[86] P. G. Pilar, A. P. Marta, and A. Antonio, “Profit efficiency and its determinants in small and medium-sized enterprises in Spain,” BRQ Business Research Quarterly, vol. 21, no. 4, pp. 238–250, Oct. 2018, 10.1016/J.BRQ.2018.08.003.

[87] E. Farrow, “To augment human capacity— Artificial intelligence evolution through causal layered analysis,” Futures, vol. 108, pp. 61–71, Apr. 2019,

[88] E. Falatoonitoosi, S. Ahmed, and S. Sorooshian, “A multicriteria framework to evaluate supplier’s greenness,” Abstract and Applied Analysis, vol. 2014, 2014, 10.1155/2014/396923.

[89] V. Y. Meytus, “Problems of constructing intelligent systems. Knowledge representation,” Cybern. Syst. Analysis, vol. 55, no. 4, pp. 521–530, Jul. 2019, 10.1007/s10559-019-00160-5.

[90] K. R. Hope, “Capacity development for good governance in developing countries: some lessons from the field,” Int J Public Adm, vol. 32, no. 8, pp. 728–740, 2009, 10.1080/01900690902908562.

[91] A. Edmans, “Blockholders and corporate governance,” Annual Review of Financial Economics, vol. 6, pp. 23–50, Dec. 2014, 10.1146/ANNUREV-FINANCIAL-110613- 034455.

[92] V. Khatri and C. V. Brown, “Designing data governance,” Commun ACM, vol. 53, no. 1, pp. 148–152, Jan. 2010, 10.1145/1629175.1629210.

[93] S. Roohani, Y. Furusho, and M. Koizumi, “XBRL: Improving transparency and monitoring functions of corporate governance,” International Journal of Disclosure and Governance, vol. 6, no. 4, pp. 355–369, Dec. 2009, 10.1057/JDG.2009.17.

@article{1711007,
      author = {Chizoba Michael Okafor, Blessing Olajumoke Farounbi, Ogochukwu Prisca Onyelucheya, Omoize Fatimetu Dako},
      title = {Detecting Financial Statement Irregularities: Hybrid Benford-Outlier-Process-Mining Anomaly Detection Architecture},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {5},
      pages = {312-327},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711007.pdf},
      abstract = {Financial statement irregularities continue to pose significant challenges for organizations, auditors, and regulators, often resulting in financial misreporting, regulatory sanctions, and erosion of investor confidence. Traditional detection methods, including manual audits and standard statistical tests, have proven insufficient to address complex and high-volume financial data. This paper proposes a hybrid anomaly detection architecture combining Benford?s Law, statistical outlier detection, and process mining techniques to identify irregularities in financial statements. By synthesizing insights from prior literature in accounting, auditing, data analytics, and process management, the study presents a conceptual framework for detecting anomalies across transactional, operational, and reporting layers. The proposed approach leverages the strengths of each detection method: Benford?s Law for assessing digit distribution conformity, outlier detection for identifying numerical deviations, and process mining for uncovering irregular process sequences. The integration of these techniques offers a multi-dimensional anomaly detection system, enhancing the ability to flag potential fraud, errors, or reporting inconsistencies. This paper further explores the implications of such hybrid frameworks for audit quality, financial governance, and regulatory oversight. Recommendations for future research include empirical validation, adaptation to cross-jurisdictional financial regulations, and integration with machine learning for predictive analytics.},
      keywords = {Financial Statement Irregularities, Hybrid Anomaly Detection, Benford?s Law, Outlier Detection, Process Mining, Audit Analytics},
      month = {November},
  }