Markov Chain Analysis of Bank Customer Migration: Implication for Financial Inclusion in Maritime Economies

Nahrul Hayati, Eko Sulistyono, Rani Gusrita

Abstract


Objectives: This study analyzes customer migration patterns among five major banks (BCA, BNI, BRI, BSI, and Bank Mandiri) in Batam’s strategic maritime economic zone using a Markov Chain model to assess long-term market dynamics and financial inclusion implications. The research aims to quantify interbank transition probabilities, to identify key switching drivers, and to develop targeted policy recommendations. Methods: Using a quantitative descriptive-analytical approach, we collected structured questionnaires from 250 Batam Institute of Technology academic members, capturing historical bank transitions and 5-point Likert-scale evaluations of eight switching factors. These factors included ATM/branch proximity, administrative fees, mobile/internet banking service, salary/ scholarship payment linkages, promotions/rewards, interest rates, family/friend recommendations, and Sharia compliance. Data were analyzed via Markov Chain modeling to project steady-state distributions. Results: The transition matrix revealed BCA’s superior retention (85.1%) compared to peers, with steady-state projections showing market dominance (32.44%), followed by Bank Mandiri (26.51%) and BSI (26.39%). Salary linkages (mean score: 3.45) and ATM accessibility (3.16) emerged as primary retention drivers, while BCA’s digital services (3.40) and low fee perception (3.67) explained its competitive edge. Paradoxically, BSI capitalizes on institutional salary systems (4.27) despite moderate Sharia compliance ratings (2.87). Implications: Three key policy directions emerge: hybrid digital-physical banking for coastal communities, Islamic financial ecosystem development, and fee transparency regulations. The study advances Markov Chain applications in behavioral finance while providing SEZ-specific insights for inclusive banking strategies.

Keywords


Markov Chain; Bank Customer Migration; Maritime Economy.

Full Text:

DOWNLOAD [PDF]

References


Anggraeny, I., & Ayu, I. K. (2020). Development of Indonesian Free Trade and Port Zone: Analysis of Historical in Batam Island. Journal of Law, Policy and Globalization, 99, 19–24. https://doi.org/

7176/jlpg/99-03

Aritenang, A. F., & Chandramidi, A. N. (2020). The impact of special economic zones and government intervention on firm productivity: The case of Batam, Indonesia. Bulletin of Indonesian Economic Studies, 56(2), 225–249. https://doi.org/10.1080/00074918.2019.1643005

Arshad, T., Zahra, R., & Draz, U. (2016). Impact of Customer Satisfaction on Image, Trust, Loyalty and the Customer Switching Behavior in Conventional and Islamic Banking: Evidence from Pakistan. American Journal of Business and Society, 1(3), 154–165. http://www.aiscience.org/journal/

ajbshttp://creativecommons.org/licenses/by/4.0/

Bagwan, W. (2025). Markov Chain-based Hydrological Drought Assessment for Maharashtra State (2012–2023) and District-Level Drought Risk Forecasts Until 2035. Arabian Journal for Science and Engineering, 1–9. https://doi.org/10.1007/s13369-024-09958-8

Bai, Y., Sun, S., Xu, Y., Zhao, Y., Pan, Y., Xiao, Y., & Li, R. (2025). Exploring the dynamic impact of future land use changes on urban flood disasters: A case study in Zhengzhou City, China. Geography and Sustainability, 6(4), Article 100287. https://doi.org/10.1016/j.geosus.2025.100287

Dalimunthe, D. Y., Fahria, I., & Wahyuni, D. (2023). Markov chain analysis to predict natural disasters in the Province of Bangka Belitung Islands as part of preventative measures to prevent environmental damage. IOP Conference Series: Earth and Environmental Science, 1267(1), 1–6. https://doi.org/10.1088/1755-1315/1267/1/012010

Danaa, A. A. A., Daabo, M. I., & Abdul-Barik, A. (2021). Detecting Electronic Banking Fraud on Highly Imbalanced Data using Hidden Markov Models. Earthline Journal of Mathematical Sciences, 7, 315–332. https://doi.org/10.34198/ejms.7221.315332

Delimatsis, P. (2021). Financial Services Trade in Special Economic Zones. Journal of International Economic Law, 24(2), 277–297. https://doi.org/10.1093/jiel/jgab023

Dung, D., Quang, N., & Chi, B. (2022). A markov chain model for predicting brand switching behavior toward online food delivery services. International Econometric Conference of Vietnam, 781–797. https://doi.org/10.1007/978-3-030-98689-6_51

Fei, X., You, Y., & Yang, X. (2020). “We” are different: Exploring the diverse effects of friend and family accessibility on consumers’ product preferences. Journal of Consumer Psychology, 30(3), 543–550. https://doi.org/10.1002/jcpy.1152

Ghamry, S., & Shamma, H. M. (2022). Factors influencing customer switching behavior in Islamic banks: evidence from Kuwait. Journal of Islamic Marketing, 13(3), 688–716. https://doi.org/10.1108/

JIMA-01-2020-0021

Gupta, R. K., Khan, D., & Banerjee, S. (2025). Markovian Brand Switching Model for Long-Term Steady-State Market Shares: A Study on Toothpaste Market. In Decision Making Under Uncertainty Via Optimization, Modelling, and Analysis (Vol. 558, pp. 567–581). Springer. https://doi.org/10.1007/978-981-96-0085-4_30

Gupta, R. K., Khan, D., Banerjee, S., & Samanta, F. (2020). An Application of Markovian Brand Switching Model to Develop Marketing Strategies in Sunscreen Market with Special Emphasis on the Determination of Long Run Steady State Market Shares. International Journal of Applied Marketing & Management, 5(1&2), 21–27. https://www.researchgate.net/publication/348048821_

An_Application_of_Markovian_Brand_Switching_Model_to_Develop_Marketing_Strategies_in_Sunscreen_Market_with_Special_Emphasis_on_the_Determination_of_Long_Run_Steady_State_Market_Shares

Hayati, N., Setiawaty, B., & Purnaba, I. (2023). The Application of Discrete Hidden Markov Model on Crosses of Diploid Plant. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 17(3), 1449–1462. https://doi.org/10.30598/barekengvol17iss3pp1449-1462

Hayati, N., Sulistyono, E., & Aprilla Handayani, V. (2024). Utilizing Discrete Hidden Markov Models To Analyze Tetraploid Plant Breeding. Jurnal Matematika UNAND, 13(4), 244–256. https://doi.org/10/25077/jmua.13.4.244-256.2024

Hutchinson, F. E. (2019). Rowing Against the Tide: Batam’s Economic Fortunes in Today’s Indonesia. ISEAS-Yusof Ishak Institute.

Jena, S., Sahoo, R. K., & Sahoo, P. (2022). Markov Chain Model in Measure Brand Swithcing of Bath Soap. International Journal of Creative Research Thoughts (IJCRT), 10(4), 241–250. https://

www.researchgate.net/publication/360399784_Markov_chain_model_in_measure_brand_switching_of_bath_soap

Kamaruzzaman, M., Kabir, M., Rahman, A., Jahan, C., Mazumder, Q., & Rahman, M. (2018). Modeling of agricultural drought risk pattern using Markov chain and GIS in the western part of Bangladesh. Environment, Development and Sustainability, 20, 569–588. https://doi.org/10.1007/s10668-016-9898-0

Kanz, M., Breza, E., & Klapper, L. (2020). Learning to navigate a new financial technology: Evidence from payroll accounts (NBER Working Paper No. 28249; CEPR Discussion Paper No. DP15565). National Bureau of Economic Research; Centre for Economic Policy Research. www.cepr.org

Kar, S. K., Panigrahi, D., & Sharma, N. (2021). Millennials’ Brand Switching Behavior in the Indian Online Retail using Markov Chain. IUP Journal of Marketing Management, 20(4). https://openurl.ebsco.com/EPDB%3Agcd%3A7%3A15807431/detailv2?sid=ebsco%3Aplink%3Ascholar&id=ebsco%3Agcd%3A154745304&crl=c&link_origin=scholar.google.com

Kim, L., & Jindabot, T. (2021). Key determinants on switching intention in Cambodian banking market. ABAC Journal, 41(2), 204–222. https://assumptionjournal.au.edu/index.php/abacjournal/article/view/4361

Kronenberg, R. P., & Khor, H. E. (2016). Economic growth in the Pacific Island Countries—challenges, constraints, and policy responses. In Resilience and Growth in the Small States of the Pacific (Vol. 10, pp. 1–14). International Monetary Fund.

Negara, S. D., & Hutchinson, F. E. (2018). Batam: Life after the FTZ? Bulletin of Indonesian Economic Studies, 56(1), 87–125. https://doi.org/10.1080/00074918.2019.1648752

Neger, M., Hossain, A., Zakir, M., Bhuiyan, H., Humayun, M., & Chowdhury, K. (2021). Markov Analysis for Assessing Consumers’ Brand Switching Behavior: Evidence from Telecommunication Sector in Bangladesh. In Print) International Journal of Education and Social Science (Vol. 8, Issue 3). Online. www.ijessnet.com

Nurani, G., Rimenda, T., Juwita, R., & Abrianto, H. (2024). “Ajak Teman” as a referral strategy to persuade friends to use digital banking. KnE Social Sciences, 2024, 647–662. https://doi.org/10.18502/kss.v9i14.16135

Odhiambo, J., Weke, P. G. O., Ngare, P., Odhiambo, J., & Weke, P. (2020). Modeling Kenyan Economic Impact of Corona Virus in Kenya Using Discrete-Time Markov Chains. Journal of Finance and Economics, 8(2), 80–85. https://doi.org/10.12691/jfe-8-2-5

Rusdiana, S., Rusyana, A., & Raja Ahmad Alfaruqi, T. (2020). Application of the stochastic transition matrix as a prediction pattern for the occurrence of disasters in Aceh province in 2017-2021. Journal of Physics: Conference Series, 1490(1), 1–6. https://doi.org/10.1088/1742-6596/1490/1/012055

Seabrook, E., & Wiskott, L. (2023). A tutorial on the spectral theory of Markov chains. Neural Computation, 35(11), 1713–1796. https://doi.org/10.1162/neco_a_01611

Song, C., Wang, T., Brown, H. T., & Hu, M. Y. (2020). The role of tie strength in bank credit card referral reward programs with scarcity messages. International Journal of Bank Marketing, 38(2), 296–309. https://doi.org/10.1108/IJBM-02-2019-0070

Thaichon, P., Quach, S., & Bavalur, A. S. (2017). Managing customer switching behavior in the banking industry. Services Marketing Quarterly, 38(3), 142–154. https://doi.org/10.1080/15332969.2017.1325644

Vincenzo, Y., & Jayadi, R. (2023). Important Factors That Affect Customer Satisfaction with Digital Banks in Indonesia. Journal of Theoretical and Applied Information Technology, 28(4), 1341–1352. www.jatit.org

Wang, X., Wu, H., & Yi, Z. (2018). Research on Bank Anti-Fraud Model Based on K-Means and Hidden Markov Model. IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 780–784. https://doi.org/10.1109/ICIVC.2018.8492795

Zhao, C., Noman, A. H. M., & Asiaei, K. (2022). Exploring the reasons for bank-switching behavior in retail banking. International Journal of Bank Marketing, 40(2), 242–262. https://doi.org/

1108/IJBM-01-2021-0042




DOI: https://doi.org/10.31764/jtam.v9i4.32121

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Nahrul Hayati, Eko Sulistyono, Rani Gusrita

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

_______________________________________________

JTAM already indexing:

                     


_______________________________________________

 

Creative Commons License

JTAM (Jurnal Teori dan Aplikasi Matematika) 
is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

______________________________________________

_______________________________________________

_______________________________________________ 

JTAM (Jurnal Teori dan Aplikasi Matematika) Editorial Office: