Process Discovery and Process Optimization of Banking Industry through Visual Mapping and Simulation of Activity Sequences

Main Article Content

Parham Porouhan

Abstract

Due to the fact that the banking industry is increasingly interested in process improvement to increase competitiveness and market share, the main focus of the paper is on how an anonymous bank can conduct an internal process improvement analysis in order to enhance its competitiveness and increase its market share, with the intention of identifying and eliminating existing or potential banking bottlenecks and enhancing the overall efficiency of the customer complaint support network. To do this, several Process Mining techniques were applied on anonymous private bank data through Fluxicon Disco, a popular process mining tool/platform. By using various filtering and clustering techniques, the process maps, and resources/attributes, to resolve customer incidents and inefficient points, were examined in detail. The generated process maps and simulation of the procedures provided a comprehensive understanding of the customer complaints journey from start to finish, which helped us identify areas of concern and propose solutions. Our analysis revealed several issues, including bottlenecks, departmental sluggishness, violation of rules/norms, and assignment concerns that have led to a lengthier and more time-consuming operational and management process from both customer and organizational perspectives. The analytical results and insightful findings of the study can be utilized by the decision makers in the Anonymous Bank to determine whether their procedures are efficient, effective and productive, as well as whether their new IT systems are functioning properly. Eventually, the study demonstrates the usefulness of process mining techniques in improving operational efficiency and customer satisfaction. Banks and financial institutions can use the findings of this study to optimize their processes, reduce operational costs, and enhance customer satisfaction, which will ultimately contribute to their overall competitiveness and market share in the industry.

Article Details

How to Cite
1.
Porouhan P. Process Discovery and Process Optimization of Banking Industry through Visual Mapping and Simulation of Activity Sequences. Prog Appl Sci Tech. [Internet]. 2023 May 26 [cited 2024 May 17];13(2):59-6. Available from: https://ph02.tci-thaijo.org/index.php/past/article/view/247758
Section
Information and Communications Technology

References

Cacciola G, Conforti R, Nguyen H. Rabobank: A Process Mining Case Study. BPI Challenge 2014 Report. Available from: https://www.win.tue.nl/bpi/2014/bpic2014_submission_5.pdf

Van Dongen B. BPI Challenge 2014: Activity log for incidents (Version 1) [Data set]. Rabobank Nederland; 2014. doi: 10.4121/uuid:86977bac-f874-49cf-8337-80f26bf5d2ef.

Van Dongen BF. BPI Challenge 2014. Version 1. 4TU.ResearchData. collection. doi: 10.4121/uuid:c3e5d162-0cfd-4bb0-bd82-af5268819c35.

Cacciola G, Conforti R, Nguyen H. Rabobank: a process mining case study BPI challenge 2014 report. BPI Challenge. 2014;1-3. doi: 10.13140/RG.2.1.3427.5681.

Lopes IF, Ferreira DR. A survey of process mining competitions: the BPI challenges 2011–2018. Business Process Management Workshops: BPM 2019 International Workshops, 2019;362 doi: 10.1007/978-3-030-37453-2_22.

Cortellessa V, Iazeolla G. Performance analysis of optimistic parallel simulations with limited rolled back events. Simulation Practice and Theory . 1999;7(4):325-347.

Rozinat A. Installation Instructions for Academic Partners [Internet]. Last updated: 26 July 2013. Fluxicon; 2013. Available from: https://fluxicon.com/academic/material/files/Installation.pdf

Bleisinger O, Cobra JPC. Machine Learning Based Simulation for Wear Estimation in Commercial Vehicle Applications. Commercial Vehicle Technology 2022: Proceedings of the 7th International Commercial Vehicle Technology Symposium; 2023: Springer.

Dongen BFV. BPI Challenge 2014. 2014. 4th International Business Process Intelligence Challenge (BPIC’14), Haifa, Israel. doi: 10.4121/uuid:c3e5d162-0cfd-4bb0-bd82-af5268819c35.

Gunther CW, Van Der Aalst WM. Fuzzy mining - adaptive process simplification based on multi-perspective metrics. Lecture Notes in Computer Science. 2007;4714:328-343.

Van Der Aalst W. Process mining: discovery, conformance and enhancement of business processes: Springer; 2011.

Porouhan P, Premchaiswadi W. Process Mining and Learners' Behavior Analytics in a Collaborative and Web-Based Multi-Tabletop Environment. Int J Online Pedagogy Course Des. 2017;7(3):29-53. doi: 10.4018/IJOPCD.2017070103.

Porouhan P, Premchaiswadi W. Behavioral Performance Evaluation and Emotion Analytics of a MOOC Course via Fuzzy Modeling. 16th International Conference on ICT and Knowledge Engineering (ICT&KE); 2018. Bangkok, Thailand; 2018;1-8. doi: 10.1109/ICTKE.2018.8612402.

Porouhan P, Premchaiswadi W. Big Data Analytics of Supply Chains with Process Mining. 19th International Conference on ICT and Knowledge Engineering (ICT&KE); 2021. Bangkok, Thailand; 2021;1-5. doi: 10.1109/ICTKE52386.2021.9665705.

Premchaiswadi W, Porouhan P, Premchaiswadi N. Process modeling, behavior analytics and group performance assessment of e-learning logs via fuzzy miner algorithm. 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC); 2018;304-309 doi: 10.1109/COMPSAC.2018.10247.

Porouhan P, Premchaiswadi W, editors. Development of a process-aware instructor-aware multi-tabletop collaborative learning environment. 2016 14th International Conference on ICT and Knowledge Engineering (ICT&KE); 2016;62-70 doi: 10.1109/ICTKE.2016.7804100.

Arpasat P, Premchaiswadi N, Porouhan P, Premchaiswadi W. Applying process mining to analyze the behavior of learners in online courses. Int. J. Inf. Educ. Technol. 2021;11(10):436-443.

Rozinat A. ProM Tips—Which Mining Algorithm Should You Use [Internet]. Fluxicon BV; [cited 2023 May 13]. Available from: https://fluxicon.com/blog/2010/10/prom-tips-mining-algorithm/

Günther CW. Disco 3.3 [Internet]. Fluxicon BV; 2022 Oct 21 [cited 2023 May 13]. Available from: https://fluxicon.com/blog/2022/10/disco-3-3/.

Weijters A, van der Aalst W, van Dongen B, Günther C, Mans R, De Medeiros AA, et al. Process mining with ProM. 19th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2007); 2007.

Nammakhunt A, Romsaiyud W, Porouhan P, Premchaiswadi W. Process mining: Converting data from MS-Access Database to MXML format. 2012 Tenth International Conference on ICT and Knowledge Engineering; 2012;205-212. doi: 10.1109/ICTKE.2012.6408557.

Data Requirements [Internet]. Fluxicon BV; [cited 2023 May 13]. Available from: https://fluxicon.com/book/read/dataext/