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INVESTIGATION OF PROCESS MINING APPLICATIONS IN THE BANKING SECTOR AND ITS EFFECTS ON THE PROCESSES USING THE MONTE CARLO METHOD

Yıl 2025, Cilt: 7 Sayı: 2, 185 - 201, 28.02.2025
https://doi.org/10.56809/icujtas.1506602

Öz

In this study, general information about process mining is given, and a process mining application is made using the cash withdrawal process data of a bank operating in the banking sector. With the application, bottlenecks in the process were identified. It is expected that by removing these bottlenecks from the process, transactions can be completed in a shorter time. To demonstrate this quantitatively, the one-year average transaction times of 22 branches of a bank, daily transactions, customers, number of transactions and average durations were modeled and daily transactions were stochastically simulated with the Monte Carlo method. The effect of removing bottlenecks based on process mining has been applied to the model and it has been shown quantitatively that the daily processing time decreases with the application of process mining. As a result, improvements can be made in process development studies by using process mining software in the banking sector.

Kaynakça

  • Burattin, A. (2015). Process Mining Techniques in Business Envirionments, Springer.
  • Crum, M., Rayhorn, C. (2019). Using Monte Carlo Simulation for Pro Forma Financial Statements .Journal of Accounting and Finance Vol. 19(5) pp.29-40. 2019.
  • Erdoğan, T. (2018). Sağlık Süreçlerini İyileştirmede Süreç Madenciliği Tekniğini Kullanan Bir Performans Analiz Metodu. [Yüksek Lisans Tezi]. Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.
  • Gartner. (2023) Analyst Report: 2023 Gartner® Magic Quadrant™ for Process Mining Tools. https://www.softwareag.com/en_corporate/platform/aris/process-mining-tools-gartner report.html?utm_source=google&utm_medium=cpc&utm_campaign=swag brand_umbrella&utm_region=hq&utm_subcampaign=stg-1&utm_content=stg-1_webpage_simplify_the_conn_world_gartner_lp_abm en son erişim tarihi 06 Nisan 2024
  • Gomé, S., Tuckerman, L., Barkley, D. (2022). Extreme events in transitional turbulence. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2022, 380 (2226), pp.20210036. ff10.1098/rsta.2021.0036ff. ffhal-03796501f
  • Günther, C. W. (2009). Process Mining in Flexible Environments Ph.D. Thesis, Technische Universiteit Eindhoven.
  • Helm, E., & Paster, F. (2015). First Steps Towards Process Mining in Distributed Health Information Systems. International Journal of Electronics and Telecommunications. https://doi.org/10.1515/eletel-2015-0017 Kebede, M. (2015). Comparative Evaluation of Process Mining Tools. University of Tartu.
  • Kang, C. J., Y. S. Kang, Y. S. Lee, S. Noh, H. C. Kim, W. C. Lim, J. Kim and R. Hong (2013). Process Mining-based Understanding and Analysis of Volvo IT's Incident and Problem Management Processes. BPIC@ BPM.
  • Kumaraguru, P. V. (2013). Machine learning approach for model discovery and process enhancement using process mining techniques Ph.D. Thesis, Dr. M.G.R. Educational and Research Institute. M. Bozkaya, J. Gabriels, and J. M. van der Werf, “Process Diagnostics: A Method Based on Process Mining” 2009 International Conference on Information, Process and Knowledge Management, Cancun Şub. 2009, pp 22-2. Doi:10.1109/Eknow.2009.29.
  • Kuşakçı, A.O., Ayvaz, B., Borat, O. (2022). Mühendisler İçin Sistem Benzetimi. Genişletilmiş 2. Basım, Nobel Akademik Yayıncılık, Ankara. ISB: 978-625-417-172-7.
  • Sevim Ş. (2021). Süreç Madenciliği Yönetimi ile Satın Alma Sürecinin Analiz Edilmesi. [Yüksek Lisans Tezi]. Sakarya Üniversitesi, Fen Bilimleri Enstitüsü, Sakarya.
  • Simsek, K.D. (2024). Monte Carlo Simulation in Financial Modeling. The Journal of Portfolio Management Quantitative Tools 2023, 49 ( 9) 178 – 188. DOI: 10.3905/jpm.2023.1.521
  • Ufuk Çelik , Eyüp Akçetin , (2018), AJIT-e: Academic Journal of Information Technology, Cilt 9, Sayı 34, 2018, 97
  • Van der Aalst, W., A. Adriansyah, A. K. A. de Medeiros, F. Arcieri, T. Baier, et al (2012). Process Mining Manifesto. Business Process Management Workshops: BPM 2011 International Workshops, Clermont-Ferrand, France, August 29, 2011, Revised Selected Papers, Part I. F. Daniel, K. Barkaoui and S. Dustdar: 169-194. Berlin, Heidelberg, Springer Berlin Heidelberg. Van der Aalst, W., A. Adriansyah and B. van Dongen (2012). Replaying history on process models for conformance checking and performance analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2(2): 182-192.
  • Van der Aalst, W. (2004). Process Mining: A Research Agenda. Computers in Industry, 53: 231-244.
  • Van der Aalst, W. (2012a). Process Mining: overview and Opportunities. Transactions on Management Information Systems, 3(2): 99-114.
  • Van der Aalst, W. (2012b). What Makes a Good Process Model? Software & System Modelling, 11(4): 557-569.
  • Van der Aalst, W. M. P. (2014). Process Mining in the Large: A Tutorial. Business Intelligence: Third European Summer School, eBISS 2013, Dagstuhl Castle, Germany, July 7-12, 2013, Tutorial Lectures. E. Zimányi: 33-76. Cham, Springer International Publishing.
  • Van der Aalst, W. M. P., A. K. A. de Medeiros and A. J. M. M. Weijters (2005). Genetic Process Mining. Applications and Theory of Petri Nets 2005: 26th International Conference, ICATPN 2005, Miami, USA, June 20-25, 2005. Proceedings. G. Ciardo and P. Darondeau: 48-69. Berlin, Heidelberg, Springer Berlin Heidelberg.
  • Van der Aalst, W. M. P., H. A. Reijers, A. J. M. M. Weijters, B. F. van Dongen, A. K. Alves de Medeiros, M. Song and H. M. W. Verbeek (2007). Business process mining: An industrial application. Information Systems 32(5): 713-732.
  • Van der Aalst, W. M. P., H. T. de Beer and B. F. van Dongen (2005). Process Mining and Verification of Properties: An Approach Based on Temporal Logic. On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2005, Agia Napa, Cyprus, October 31 - November 4, 2005, Proceedings, Part I. R. Meersman and Z. Tari: 130-147. Berlin, Heidelberg, Springer Berlin Heidelberg. Van der Aalst, W., Reijers, H., Weijters, A., Dongen, B., Medeiros, A., Song, M., Verbeek, H. (2006). Business Process Mining: An Industrial Application. Information Systems, 32(5): 713-732.
  • Van der Aalst, W., T. Weijters and L. Maruster (2004). Workflow mining: Discovering process models from event logs. Knowledge and Data Engineering, IEEE Transactions on 16(9): 1128-1142. Van der Aalst, W. M., B. F. Van Dongen, J. Herbst, L. Maruster, G. Schimm and A. J. Weijters (2003). Workflow mining: a survey of issues and approaches. Data & knowledge engineering 47(2): 237-267.
  • Yılmaz, Y. (2019). Business Process Reengineering Using Process Mining. [Yüksek Lisans Tezi]. Boğaziçi Üniversitesi, Sosyal Bilimler Enstitüsü, İstanbul.
  • Yousfi, A., Weske, M. (2019). Discovering Commute Patterns via Process Mining, Knowledge & Information System,60(2):691-713.–104.
  • W. M. P. van der Aalst, B. F. van Dongen, J. Herbst, L. Maruster, G. Schimm, and A. J. M. M. Weijters, “Workflow mining: A survey of issues and approaches,” Data & Knowledge Engineering, vol. 47, no 2, pp. 237-267, Kas. 2003,doi:10.1016/S0169-023X(03)00066-1
  • Xu,D.L., Yue, P., Yi, X., and Liu,J.Y. (2022). Improvement of a Monte-Carlo-simulation-based turbulence-induced attenuation model for an underwater wireless optical communications channel. Journal of the Optical Society of America A Vol. 39, Issue 8, pp. 1330-1342 (2022) https://doi.org/10.1364/JOSAA.459753

BANKACILIK SEKTÖRÜNDE SÜREÇ MADENCİLİĞİ UYGULAMALARI VE SÜREÇLERE ETKİLERİNİN MONTE CARLO YÖNTEMİYLE İNCELENMESİ

Yıl 2025, Cilt: 7 Sayı: 2, 185 - 201, 28.02.2025
https://doi.org/10.56809/icujtas.1506602

Öz

Bu çalışmada süreç madenciliği ile ilgili genel bilgiler verilmiş olup, bankacılık sektöründe faaliyet gösteren bir bankanın nakit çekme süreç verileri kullanılarak süreç madenciliği uygulaması yapılmıştır. Uygulama ile süreçte bulunan dar boğazlar tespit edilmiştir. Bu dar boğazların süreçten kaldırılması ile işlemlerin daha kısa zamanda yapılabileceği beklenmektedir. Bunu kantitatif olarak göstermek için bir bankanın 22 şubesinin bir yıllık ortalama işlem süreleri günlük işlemler, müşteriler, işlem sayıları ve ortalama süreleri modellenmiş ve günlük işlemler Monte Carlo yöntemi ile stokastik olarak simüle edilmiştir. Süreç madenciliğine dayanan dar boğazların kaldırılma etkisi modele uygulanmış ve süreç madenciliği uygulaması ile günlük işlem süresinin azaldığı nicel olarak gösterilmiştir. Sonuç olarak süreç madenciliği yazılımlarının bankacılık sektöründe kullanımı ile süreç geliştirme çalışmalarında iyileştirmeler yapılabilecektir.

Kaynakça

  • Burattin, A. (2015). Process Mining Techniques in Business Envirionments, Springer.
  • Crum, M., Rayhorn, C. (2019). Using Monte Carlo Simulation for Pro Forma Financial Statements .Journal of Accounting and Finance Vol. 19(5) pp.29-40. 2019.
  • Erdoğan, T. (2018). Sağlık Süreçlerini İyileştirmede Süreç Madenciliği Tekniğini Kullanan Bir Performans Analiz Metodu. [Yüksek Lisans Tezi]. Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.
  • Gartner. (2023) Analyst Report: 2023 Gartner® Magic Quadrant™ for Process Mining Tools. https://www.softwareag.com/en_corporate/platform/aris/process-mining-tools-gartner report.html?utm_source=google&utm_medium=cpc&utm_campaign=swag brand_umbrella&utm_region=hq&utm_subcampaign=stg-1&utm_content=stg-1_webpage_simplify_the_conn_world_gartner_lp_abm en son erişim tarihi 06 Nisan 2024
  • Gomé, S., Tuckerman, L., Barkley, D. (2022). Extreme events in transitional turbulence. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2022, 380 (2226), pp.20210036. ff10.1098/rsta.2021.0036ff. ffhal-03796501f
  • Günther, C. W. (2009). Process Mining in Flexible Environments Ph.D. Thesis, Technische Universiteit Eindhoven.
  • Helm, E., & Paster, F. (2015). First Steps Towards Process Mining in Distributed Health Information Systems. International Journal of Electronics and Telecommunications. https://doi.org/10.1515/eletel-2015-0017 Kebede, M. (2015). Comparative Evaluation of Process Mining Tools. University of Tartu.
  • Kang, C. J., Y. S. Kang, Y. S. Lee, S. Noh, H. C. Kim, W. C. Lim, J. Kim and R. Hong (2013). Process Mining-based Understanding and Analysis of Volvo IT's Incident and Problem Management Processes. BPIC@ BPM.
  • Kumaraguru, P. V. (2013). Machine learning approach for model discovery and process enhancement using process mining techniques Ph.D. Thesis, Dr. M.G.R. Educational and Research Institute. M. Bozkaya, J. Gabriels, and J. M. van der Werf, “Process Diagnostics: A Method Based on Process Mining” 2009 International Conference on Information, Process and Knowledge Management, Cancun Şub. 2009, pp 22-2. Doi:10.1109/Eknow.2009.29.
  • Kuşakçı, A.O., Ayvaz, B., Borat, O. (2022). Mühendisler İçin Sistem Benzetimi. Genişletilmiş 2. Basım, Nobel Akademik Yayıncılık, Ankara. ISB: 978-625-417-172-7.
  • Sevim Ş. (2021). Süreç Madenciliği Yönetimi ile Satın Alma Sürecinin Analiz Edilmesi. [Yüksek Lisans Tezi]. Sakarya Üniversitesi, Fen Bilimleri Enstitüsü, Sakarya.
  • Simsek, K.D. (2024). Monte Carlo Simulation in Financial Modeling. The Journal of Portfolio Management Quantitative Tools 2023, 49 ( 9) 178 – 188. DOI: 10.3905/jpm.2023.1.521
  • Ufuk Çelik , Eyüp Akçetin , (2018), AJIT-e: Academic Journal of Information Technology, Cilt 9, Sayı 34, 2018, 97
  • Van der Aalst, W., A. Adriansyah, A. K. A. de Medeiros, F. Arcieri, T. Baier, et al (2012). Process Mining Manifesto. Business Process Management Workshops: BPM 2011 International Workshops, Clermont-Ferrand, France, August 29, 2011, Revised Selected Papers, Part I. F. Daniel, K. Barkaoui and S. Dustdar: 169-194. Berlin, Heidelberg, Springer Berlin Heidelberg. Van der Aalst, W., A. Adriansyah and B. van Dongen (2012). Replaying history on process models for conformance checking and performance analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2(2): 182-192.
  • Van der Aalst, W. (2004). Process Mining: A Research Agenda. Computers in Industry, 53: 231-244.
  • Van der Aalst, W. (2012a). Process Mining: overview and Opportunities. Transactions on Management Information Systems, 3(2): 99-114.
  • Van der Aalst, W. (2012b). What Makes a Good Process Model? Software & System Modelling, 11(4): 557-569.
  • Van der Aalst, W. M. P. (2014). Process Mining in the Large: A Tutorial. Business Intelligence: Third European Summer School, eBISS 2013, Dagstuhl Castle, Germany, July 7-12, 2013, Tutorial Lectures. E. Zimányi: 33-76. Cham, Springer International Publishing.
  • Van der Aalst, W. M. P., A. K. A. de Medeiros and A. J. M. M. Weijters (2005). Genetic Process Mining. Applications and Theory of Petri Nets 2005: 26th International Conference, ICATPN 2005, Miami, USA, June 20-25, 2005. Proceedings. G. Ciardo and P. Darondeau: 48-69. Berlin, Heidelberg, Springer Berlin Heidelberg.
  • Van der Aalst, W. M. P., H. A. Reijers, A. J. M. M. Weijters, B. F. van Dongen, A. K. Alves de Medeiros, M. Song and H. M. W. Verbeek (2007). Business process mining: An industrial application. Information Systems 32(5): 713-732.
  • Van der Aalst, W. M. P., H. T. de Beer and B. F. van Dongen (2005). Process Mining and Verification of Properties: An Approach Based on Temporal Logic. On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2005, Agia Napa, Cyprus, October 31 - November 4, 2005, Proceedings, Part I. R. Meersman and Z. Tari: 130-147. Berlin, Heidelberg, Springer Berlin Heidelberg. Van der Aalst, W., Reijers, H., Weijters, A., Dongen, B., Medeiros, A., Song, M., Verbeek, H. (2006). Business Process Mining: An Industrial Application. Information Systems, 32(5): 713-732.
  • Van der Aalst, W., T. Weijters and L. Maruster (2004). Workflow mining: Discovering process models from event logs. Knowledge and Data Engineering, IEEE Transactions on 16(9): 1128-1142. Van der Aalst, W. M., B. F. Van Dongen, J. Herbst, L. Maruster, G. Schimm and A. J. Weijters (2003). Workflow mining: a survey of issues and approaches. Data & knowledge engineering 47(2): 237-267.
  • Yılmaz, Y. (2019). Business Process Reengineering Using Process Mining. [Yüksek Lisans Tezi]. Boğaziçi Üniversitesi, Sosyal Bilimler Enstitüsü, İstanbul.
  • Yousfi, A., Weske, M. (2019). Discovering Commute Patterns via Process Mining, Knowledge & Information System,60(2):691-713.–104.
  • W. M. P. van der Aalst, B. F. van Dongen, J. Herbst, L. Maruster, G. Schimm, and A. J. M. M. Weijters, “Workflow mining: A survey of issues and approaches,” Data & Knowledge Engineering, vol. 47, no 2, pp. 237-267, Kas. 2003,doi:10.1016/S0169-023X(03)00066-1
  • Xu,D.L., Yue, P., Yi, X., and Liu,J.Y. (2022). Improvement of a Monte-Carlo-simulation-based turbulence-induced attenuation model for an underwater wireless optical communications channel. Journal of the Optical Society of America A Vol. 39, Issue 8, pp. 1330-1342 (2022) https://doi.org/10.1364/JOSAA.459753
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İş Süreçleri Yönetimi, Endüstri Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Rabia Mamuş Yemen 0000-0003-0114-7736

Oğuz Borat 0000-0002-2242-6024

Yayımlanma Tarihi 28 Şubat 2025
Gönderilme Tarihi 28 Haziran 2024
Kabul Tarihi 16 Temmuz 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 7 Sayı: 2

Kaynak Göster

APA Mamuş Yemen, R., & Borat, O. (2025). BANKACILIK SEKTÖRÜNDE SÜREÇ MADENCİLİĞİ UYGULAMALARI VE SÜREÇLERE ETKİLERİNİN MONTE CARLO YÖNTEMİYLE İNCELENMESİ. İstanbul Ticaret Üniversitesi Teknoloji Ve Uygulamalı Bilimler Dergisi, 7(2), 185-201. https://doi.org/10.56809/icujtas.1506602