mlsanityData to 14 Oct 2026

SimBank: from Simulation to Solution in Prescriptive Process Monitoring

arXiv 2506.14772 ↗First posted 28 Mar 2025cs.DB cs.LGcited by 4

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Abstract (arXiv)

Prescriptive Process Monitoring (PresPM) is an emerging area within Process Mining, focused on optimizing processes through real-time interventions for effective decision-making. PresPM holds significant promise for organizations seeking enhanced operational performance. However, the current literature faces two key limitations: a lack of extensive comparisons between techniques and insufficient evaluation approaches. To address these gaps, we introduce SimBank: a simulator designed for accurate benchmarking of PresPM methods. Modeled after a bank's loan application process, SimBank enables extensive comparisons of both online and offline PresPM methods. It incorporates a variety of intervention optimization problems with differing levels of complexity and supports experiments on key causal machine learning challenges, such as assessing a method's robustness to confounding in data. SimBank additionally offers a comprehensive evaluation capability: for each test case, it can generate the true outcome under each intervention action, which is not possible using recorded datasets. The simulator incorporates parallel activities and loops, drawing from common logs to generate cases that closely resemble real-life process instances. Our proof of concept demonstrates SimBank's benchmarking capabilities through experiments with various PresPM methods across different interventions, highlighting its value as a publicly available simulator for advancing research and practice in PresPM.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 28 Mar 2025.

Builds on

17 of 36 references.

References · 17

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2018Neural Autoregressive Flowscited by 208
  3. 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 155
  4. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  5. 2020RealCause: Realistic Causal Inference Benchmarkingcited by 46
  6. 2022Sample Efficient Deep Reinforcement Learning via Uncertainty Estimationcited by 27
  7. 2020Prescriptive Business Process Monitoring for Recommending Next Best Actionscited by 22
  8. 2019Automated Discovery of Business Process Simulation Models from Event Logscited by 17
  9. 2021Prescriptive Process Monitoring: Quo Vadis?cited by 15
  10. 2021Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approachcited by 12
  11. 2022When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraintscited by 11
  12. 2024AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulationcited by 8
5 more
  1. 2023Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learningcited by 6
  2. 2023Learning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approachescited by 5
  3. 2023Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoringcited by 4
  4. 2023Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approachcited by 4
  5. 2021A Python Extension to Simulate Petri nets in Process Miningcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methodscited by 2
  2. 2025SCOPE: Sequential Causal Optimization of Process Interventionscited by 0
  3. 2026Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoringcited by 0
  4. 2026From Event Logs to Governed Action: A BlueSky Agenda for Agentic Process Miningcited by 0