Framework of Master Data Management in Banking Using Consolidation and Jaro Winkler Algorithm

Ipang Sasono; Mustar Aman

doi:10.34306/ijcitsm.v5i2.212

Authors

Ipang Sasono Universitas Insan Pembangunan Indonesia https://orcid.org/0000-0002-0345-4206
Mustar Aman Universitas Insan Pembangunan Indonesia https://orcid.org/0000-0002-0507-6122

DOI:

https://doi.org/10.34306/ijcitsm.v5i2.212

Keywords:

Master Data Management, Consolidation Approach, Jaro-Winkler Algorithm, Data Integration, Banking Systems

Abstract

Master Data Management (MDM) is a crucial framework for ensuring the consistency, accuracy, and reliability of key data entities within banking systems. In the financial sector, where data from multiple departments and sources is constantly generated and shared, it is vital to maintain a single, unified view of critical data to prevent inconsistencies, inaccuracies, and duplication. This paper introduces a comprehensive design for implementing MDM in banks, utilizing a consolidation approach integrated with the Jaro-Winkler similarity algorithm. The consolidation approach allows the seamless integration of disparate data sources across various departments, creating a unified and centralized data repository. This is essential for maintaining a comprehensive and reliable view of data assets, thereby improving decision-making and operational efficiency. The inclusion of the Jaro-Winkler algorithm enhances data matching capabilities by identifying and resolving duplicates or near-duplicate records through name and text similarity comparisons, an essential feature given the complex nature of customer and transactional data in banking. By addressing these challenges, the proposed MDM solution significantly improves data quality, reduces redundancy, and ensures that information is accurate and accessible across all levels of the organization. This system provides a scalable, robust, and efficient data management infrastructure, crucial for meeting regulatory compliance requirements, enhancing customer service, and optimizing operational processes. The methodology presented in this paper demonstrates an effective and structured approach for large-scale data integration and verification, offering a reliable solution for managing vast amounts of data in the banking sector.

References

D. Amsterdam, “Perspective: limiting antimicrobial resistance with artificial intelligence/machine learning,” BME frontiers, vol. 4, p. 0033, 2023.

I. Y. Kusuma, “Revolutionizing the fight against antimicrobial resistance with artificial intelligence,” Pharmacy Reports, vol. 3, no. 1, pp. 53–53, 2023.

D. E. E. Saputra, D. Susita, A. Eliyana, A. S. Pratama, M. H. Muzakki, and Z. Yazid, “Employees intentions to use performance management system in regional bank: Perspective from generation-x,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 550–561, 2024.

H. Woo and S. Y. Sohn, “A credit scoring model based on the myers–briggs type indicator in online peer-to-peer lending,” Financial innovation, vol. 8, no. 1, p. 42, 2022.

P. Malani, “Highlights from idweek 2023—new vaccines, artificial intelligence, and antimicrobial resistance,” JAMA, vol. 330, no. 21, pp. 2040–2041, 2023.

F. Branda, “The impact of artificial intelligence in the fight against antimicrobial resistance,” Infectious Diseases, vol. 56, no. 6, pp. 484–486, 2024.

A. Pambudi, R. Widayanti, and P. Edastama, “Trust and acceptance of e-banking technology: Effect of mediation on customer relationship management performance,” Journal on Recent Innovation, vol. 3, no. 1, pp. 86–95, 2021.

C. S. R. Adapa, “Building a standout portfolio in master data management (mdm) and data engineering,” International Research Journal of Modernization in Engineering Technology and Science, vol. 7, no. 3, pp. 8082–8099, 2025.

M.-H. Tran, N. Q. Nguyen, and H. T. Pham, “A new hope in the fight against antimicrobial resistance with artificial intelligence,” Infection and Drug Resistance, pp. 2685–2688, 2022.

A. Firasati, F. Azzahra, S. R. P. Junaedi, A. Evans, M. Madani, and F. P. Oganda, “The role information technology in increasing the effectiveness accounting information systems and employee performance,” International Journal of Cyber and IT Service Management, vol. 4, no. 2, pp. 114–121, 2024.

T. Ali, S. Ahmed, and M. Aslam, “Artificial intelligence for antimicrobial resistance prediction: challenges and opportunities towards practical implementation,” Antibiotics, vol. 12, no. 3, p. 523, 2023.

C. K. Ng, “Generative adversarial network (generative artificial intelligence) in pediatric radiology: A systematic review,” Children, vol. 10, no. 8, p. 1372, 2023.

N. S. Egbuhuzor, A. J. Ajayi, E. E. Akhigbe, O. O. Agbede, C. P.-M. Ewim, and D. I. Ajiga, “Cloud-based crm systems: Revolutionizing customer engagement in the financial sector with artificial intelligence,” International Journal of Science and Research Archive, vol. 3, no. 1, pp. 215–234, 2021.

F. Ariyanto, N. P. L. Santoso, M. F. Kamil, and U. Rahardja, “Innovative mobile banking solutions powered by 5g: Ensuring security and seamless connectivity,” in 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2024, pp. 01–05.

S. Stefani, “Combatting antimicrobial resistance: A year-round endeavor for global health,” p. 369, 2023.

A. Ngueilbaye, H. Wang, D. A. Mahamat, I. A. Elgendy, and S. B. Junaidu, “Methods for detecting and correcting contextual data quality problems,” Intelligent Data Analysis, vol. 25, no. 4, pp. 763–787, 2021.

E. Pradivta, F. P. Oganda, E. T. Persada, U. Rahardja et al., “Scalability and security challenges of cloud computing in the banking industry,” in 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2024, pp. 1–6.

C. Jacobs and A. van der Merwe, “Artificial intelligence for enhanced master data quality management in enterprise resource planning systems,” in Annual Conference of South African Institute of Computer Scientists and Information Technologists. Springer, 2025, pp. 164–185.

E. A. Nabila, S. Santoso, Y. Muhtadi, and B. Tjahjono, “Artificial intelligence robots and revolutionizing society in terms of technology, innovation, work and power,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 3, no. 1, pp. 46–52, 2021.

R. S. Singer, “Erratum to ‘continued abuse of causal inference in studies of antimicrobial resistance: revisiting the confusion between ecological correlation and causation’[journal of global antimicrobial resistance 30 (2022) 485-486],” Journal of Global Antimicrobial Resistance, vol. 32, p. 197, 2023.

D. M. Livermore, “Jac-antimicrobial resistance,” J Antimicrob Chemother, vol. 3, no. 1, pp. i5–i16, 2021.

N. Nuryani, A. B. Mutiara, I. M. Wiryana, D. Purnamasari, and S. N. W. Putra, “Artificial intelligence model for detecting tax evasion involving complex network schemes,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 339–356, 2024.

K. Mazayo, S. Agustina, and R. Asri, “Application of digital technology risk management models in banking institutions reflecting the digital transformation of indonesian banking blueprint,” International Journal of Cyber and IT Service Management, vol. 3, no. 2, pp. 130–143, 2023.

S. Kosasi, U. Rahardja, I. D. A. E. Yuliani, R. Laipaka, B. Susilo, and H. Kikin, “It governance: Performance assessment of maturity levels of rural banking industry,” in 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2022, pp. 1–6.

Z. Ozherelieva and A. Gulyaeva, “Resistance of generative organs of sweet cherry to spring frosts after artificial freezing,” South of Russia-Ecology Development, vol. 16, no. 2, pp. 45–54, 2021.

L. Liu, Y. Huang, Y. Wang, Y. Jiang, K. Liu, Z. Pei, Z. Li, Y. Zhu, D. Liu, and X. Li, “Molecular epidemiology and genetic characterization of carbapenem-resistant acinetobacter baumannii isolates from the icu of a tertiary hospital in east china,” Infection and Drug Resistance, pp. 5925–5945, 2024.

R. Aprianto, R. Haris, A. Williams, H. Agustian, and N. Aptwell, “Social influence on ai-driven air quality monitoring adoption: Smartpls analysis,” Sundara Advanced Research on Artificial Intelligence, vol. 1, no. 1, pp. 28–36, 2025.

O. Rozinek and J. Mareˇs, “Fast and precise convolutional jaro and jaro-winkler similarity,” in 2024 35th Conference of Open Innovations Association (FRUCT). IEEE, 2024, pp. 604–613.

K. Arora, M. Faisal et al., “The use of data science in digital marketing techniques: Work programs, performance sequences and methods.” Startupreneur Business Digital (SABDA Journal), vol. 1, no. 2, pp. 143–155, 2022.

F. Yang, X. Wen, A. Aziz, and A. K. Luhach, “The need for local adaptation of smart infrastructure for sustainable economic management,” Environmental Impact Assessment Review, vol. 88, p. 106565, 2021.

O. Hamza, A. Collins, A. Eweje, and G. O. Babatunde, “A unified framework for business system analysis and data governance: Integrating salesforce crm and oracle bi for cross-industry applications,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 4, no. 1, pp. 653–667, 2023.

P. H. P. Tan, A. Rizky, Q. Aini, D. N. Ramadhan, and T. Green, “Utilizing the alphasign website to create blockchain-based or online digital signatures,” Blockchain Frontier Technology, vol. 5, no. 1, pp. 25–36, 2025.

S. Ren, “Optimization of enterprise financial management and decision-making systems based on big data,” Journal of Mathematics, vol. 2022, no. 1, p. 1708506, 2022.

A. Kristian, T. S. Goh, A. Ramadan, A. Erica, and S. V. Sihotang, “Application of ai in optimizing energy and resource management: Effectiveness of deep learning models,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 99–105, 2024.

P. Karapanagiotis and M. Liebald, “Entity matching with similarity encoding: A supervised learning recommendation framework for linking (big) data,” SAFE Working Paper, Tech. Rep., 2023.

A. Ekawaty, E. A. Nabila, S. A. Anjani, U. Rahardja, and S. Zebua, “Utilizing sentiment analysis to enhance customer feedback systems in banking,” in 2024 12th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2024, pp. 1–6.

M. G. Hardini, N. A. Yusuf, A. R. A. Zahra et al., “Convergence of intelligent networks: Harnessing the power of artificial intelligence and blockchain for future innovations,” ADI Journal on Recent Innovation, vol. 5, no. 2, pp. 200–209, 2024.

Z. Zhang, X. Cheng, Z. Xing, and Z. Wang, “Energy management strategy optimization for hybrid energy storage system of tram based on competitive particle swarm algorithms,” Journal of Energy Storage, vol. 75, p. 109698, 2024.

T. A. D. Lael and D. A. Pramudito, “Use of data mining for the analysis of consumer purchase patterns with the fpgrowth algorithm on motor spare part sales transactions data,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 4, no. 2, pp. 128–136, 2023.

T. Ranbaduge, D. Vatsalan, and M. Ding, “Privacy-preserving deep learning based record linkage,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 11, pp. 6839–6850, 2023.

N. Lutfiani, I. Sembiring, I. Setyawan, A. Setiawan, U. Rahardja, and S. Sulistio, “Exploring the relationship between artificial intelligence and business performance,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 19, no. 1, pp. 1–12, 2025.

O. Bulut, G. Gorgun, T. Wongvorachan, and B. Tan, “Rapid guessing in low-stakes assessments: Finding the optimal response time threshold with random search and genetic algorithm,” Algorithms, vol. 16, no. 2, p. 89, 2023.

L. Frusciante, A. Visibelli, M. Geminiani, A. Santucci, and O. Spiga, “Artificial intelligence approaches in drug discovery: towards the laboratory of the future,” Current Topics in Medicinal Chemistry, vol. 22, no. 26, pp. 2176–2189, 2022.

P. Lestiyawati, “Analytic network process and tawhidi string relation in managing business of sharia commercial bank,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 1, pp. 87–98, 2023.

C. Preiksaitis and C. Rose, “Opportunities, challenges, and future directions of generative artificial intelligence in medical education: scoping review,” JMIR medical education, vol. 9, p. e48785, 2023.

M. A. Yulianto and N. Nurhasanah, “The hybrid of jaro-winkler and rabin-karp algorithm in detecting indonesian text similarity,” Jurnal Online Informatika, vol. 6, no. 1, pp. 88–95, 2021.

D. Mohammed, A. G. Prawiyog, and E. R. Dewi, “Environmental management/marketing research: Bibliographic analysis,” Startupreneur Business Digital (SABDA Journal), vol. 1, no. 2, pp. 191–197, 2022.

M. Tomar and J. Jeyaraman, “Reference data management: A cornerstone of financial data integrity,” Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), vol. 2, no. 1, pp. 137–144, 2023.

O. Azeroual, A. Nikiforova, and K. Sha, “Overlooked aspects of data governance: workflow framework for enterprise data deduplication,” in 2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS). IEEE, 2023, pp. 65–73.

T. S. Goh, D. Jonas, B. Tjahjono, V. Agarwal, and M. Abbas, “Impact of ai on air quality monitoring systems: A structural equation modeling approach using utaut,” Sundara Advanced Research on Artificial Intelligence, vol. 1, no. 1, pp. 9–19, 2025.

R. Pansara, “Design of master data architecture,” International Journal of Engineering Applied Sciences and Technology, vol. 8, no. 4, pp. 58–61, 2023.

M. Hatta, W. N. Wahid, F. Yusuf, F. Hidayat, N. A. Santoso, and Q. Aini, “Enhancing predictive models in system development using machine learning algorithms,” International Journal of Cyber and IT Service Management, vol. 4, no. 2, pp. 80–87, 2024.