AN INTEGRATED PROCESS-MINING AND EXPLAINABLE MACHINE-LEARNING FRAMEWORK FOR BOTTLENECK AND INTERNAL-CONTROL RISK DETECTION IN THE MULTI-ENTITY FINANCIAL CLOSE
Keywords:
Process mining, Explainable artificial intelligence, Internal control, Financial close, Bottleneck analysis, SHAP, Audit analyticsAbstract
The financial close of a multi-entity group is a recurring, deadline-driven process in which operational congestion and internal-control weaknesses interact, yet the two are normally analysed by separate functions with separate tools. This paper proposes CLOSE-XPM, a framework integrating process mining and explainable machine learning to attribute close-cycle delay to individual record-to-report activities, to flag close instances carrying elevated internal-control risk, and to rank remediation actions against both objectives at once. Two contributions are introduced: a cause-aware Bottleneck Contribution Index (BCI-C) that decomposes every pre-activity delay into resource contention, calendar unavailability and residual, then propagates a contention-truncation counterfactual along a per-case critical path, yielding an estimate in calendar days saved; and a screening procedure that estimates the risk effect of capacity and control actions from the event log alone. Because no public financial-close log exists, we build MEFC-Sim, a resource-aware generator (30 entities, 24 periods, 14,187 events) in which weaknesses are injected as behaviour and the outcome depends on variables the log cannot reveal; paired counterfactual re-runs provide an oracle. BCI-C reaches 0.907 rank correlation with the oracle against 0.544 for the best observable baseline, and predicts the attainable reduction within 0.076 days. Risk detection reaches 0.828 AUC across unseen entities with a 2.86-fold lift in the top-20% inspected sample. Delay and control risk are strongly coupled (rho = 0.708): relieving the three dominant bottlenecks shortens the close by 1.70 days and cuts the deficiency rate by 22.7%. A ground-truth fidelity analysis shows that SHAP is faithful to the model but only moderately faithful to the data-generating process. Validation on a real public log of 1,434 cases confirms transferability and identifies a boundary condition: coupling requires waiting time to be contention-driven.References
[1] van der Aalst W M P. Process Mining: Data Science in Action. 2nd ed. Berlin, Germany: Springer, 2016.
[2] Dumas M, La Rosa M, Mendling J, et al. Fundamentals of Business Process Management. 2nd ed. Berlin, Germany: Springer, 2018.
[3] Denisov V, Fahland D, van der Aalst W M P. Unbiased, fine-grained description of processes performance from event data. In: Business Process Management (BPM 2018), LNCS 11080. Cham, Switzerland: Springer, 2018: 139-157.
[4] Klijn E L, Fahland D. Performance mining for batch processing using the performance spectrum. In: Business Process Management Workshops (BPM 2019), LNBIP 362. Cham, Switzerland: Springer, 2019: 172-185.
[5] Senderovich A, Weidlich M, Gal A, et al. Queue mining for delay prediction in multi-class service processes. Information Systems, 2015, 53: 278-295.
[6] Fahland D, Denisov V, van der Aalst W M P. Inferring unobserved events in systems with shared resources and queues. Fundamenta Informaticae, 2021, 183(3-4): 203-242.
[7] Lashkevich K, Milani F, Chapela-Campa D, et al. Why am I waiting? Data-driven analysis of waiting times in business processes. In: Advanced Information Systems Engineering (CAiSE 2023), LNCS 13901. Cham, Switzerland: Springer, 2023: 174-190.
[8] Lashkevich K, Milani F, Chapela-Campa D, et al. Data-driven analysis of batch processing inefficiencies in business processes. In: Research Challenges in Information Science (RCIS 2022). Cham, Switzerland: Springer, 2022: 231-247.
[9] Chapela-Campa D, Dumas M. Modeling extraneous activity delays in business process simulation. In: Proceedings of the 4th International Conference on Process Mining (ICPM), 2022: 72-79.
[10] van der Aalst W M P, van Hee K M, van der Werf J M, et al. Auditing 2.0: Using process mining to support tomorrow's auditor. Computer, 2010, 43(3): 90-93.
[11] Jans M, Alles M, Vasarhelyi M. The case for process mining in auditing: Sources of value added and areas of application. International Journal of Accounting Information Systems, 2013, 14(1): 1-20.
[12] Jans M, Alles M G, Vasarhelyi M A. A field study on the use of process mining of event logs as an analytical procedure in auditing. The Accounting Review, 2014, 89(5): 1751-1773.
[13] Caron F, Vanthienen J, Baesens B. Comprehensive rule-based compliance checking and risk management with process mining. Decision Support Systems, 2013, 54(3): 1357-1369.
[14] Ramezani E, Fahland D, van der Aalst W M P. Where did I misbehave? Diagnostic information in compliance checking. In: Business Process Management (BPM 2012), LNCS 7481. Berlin, Germany: Springer, 2012: 262-278.
[15] Werner M. Financial process mining — Accounting data structure dependent control flow inference. International Journal of Accounting Information Systems, 2017, 25: 57-80.
[16] Werner M. Multilevel process mining for financial audits. IEEE Transactions on Services Computing, 2015, 8(6): 820-832.
[17] Werner M. Identifying the absence of effective internal controls: An alternative approach for internal control audits. Journal of Information Systems, 2019, 33(2): 205-222.
[18] Baader G, Krcmar H. Reducing false positives in fraud detection: Combining the red flag approach with process mining. International Journal of Accounting Information Systems, 2018, 31: 1-16.
[19] Chiu T, Jans M. Process mining of event logs: A case study evaluating internal control effectiveness. Accounting Horizons, 2019, 33(3): 141-156.
[20] Werner M, Wiese M, Maas A. Embedding process mining into financial statement audits. International Journal of Accounting Information Systems, 2021, 41: Art. 100514.
[21] Teinemaa I, Dumas M, La Rosa M, et al. Outcome-oriented predictive process monitoring: Review and benchmark. ACM Transactions on Knowledge Discovery from Data, 2019, 13(2): Art. 17.
[22] Verenich I, Dumas M, La Rosa M, et al. Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM Transactions on Intelligent Systems and Technology, 2019, 10(4): Art. 34.
[23] Kratsch M, Manderscheid J, Röglinger M, et al. Machine learning in business process monitoring: A comparison of deep learning and classical approaches used for outcome prediction. Business & Information Systems Engineering, 2021, 63(3): 261-276.
[24] Ribeiro M T, Singh S, Guestrin C. 'Why should I trust you?' Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 1135-1144.
[25] Lundberg S M, Lee S-I. A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems 30, 2017: 4765-4774.
[26] Lundberg S M. From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2020, 2(1): 56-67.
[27] Galanti R, Coma-Puig B, de Leoni M, et al. Explainable predictive process monitoring. In: Proceedings of the 2nd International Conference on Process Mining (ICPM), 2020: 1-8.
[28] Rizzi W, Di Francescomarino C, Maggi F M. Explainability in predictive process monitoring: When understanding helps improving. In: Business Process Management Forum (BPM 2020), LNBIP 392. Cham, Switzerland: Springer, 2020: 141-158.
[29] Mehdiyev N, Fettke P. Explainable artificial intelligence for process mining: A general overview and application of a novel local explanation approach for predictive process monitoring. In: Interpretable Artificial Intelligence: A Perspective of Granular Computing. Cham, Switzerland: Springer, 2021: 1-28.
[30] Bozorgi Z D, Teinemaa I, Dumas M, et al. Prescriptive process monitoring for cost-aware cycle time reduction. In: Proceedings of the 3rd International Conference on Process Mining (ICPM), 2021: 96-103.
[31] Bozorgi Z D, Teinemaa I, Dumas M, et al. Process mining meets causal machine learning: Discovering causal rules from event logs. In: Proceedings of the 2nd International Conference on Process Mining (ICPM), 2020: 129-136.
[32] Buijs J C A M. Receipt phase of an environmental permit application process ('WABO'), CoSeLoG project. Dataset. 4TU.ResearchData, 2014. DOI: 10.4121/uuid:a07386a5-7be3-4367-9535-70bc9e77dbe6.