Simulating Data Stories of Clients’ Credit Card Default

Authors

  • Siti Salwa Salleh Faculty of Computer and Mathematical Sciences, University Technology MARA (UiTM), Seremban Campus, Negeri Sembilan, Malaysia
  • Nor Ayunie Mohamed Faculty of Computer and Mathematical Sciences, University Technology MARA, Seremban Campus, Negeri Sembilan, Malaysia
  • Nur Ainin Sofea Arman Shah Faculty of Computer and Mathematical Sciences, University Technology MARA, Seremban Campus, Negeri Sembilan, Malaysia

Keywords:

Credit card, Data storytelling, Dashboard, Payment default, Sankey chart.

Abstract

Clients who have problems paying with their credit cards need to be identified. However, data analysis is rather difficult due to the numerical nature of raw data. In the digital era, it is necessary to deploy dashboards and visual items to assist users in obtaining information more quickly. Typically, the dashboard comprises relevant charts and graphics. In this study, a Sankey chart is recommended. The purpose of this project is to simulate the construction of a dashboard that supports users at credit agencies or financial institutions in detecting credit card payment defaults. User requirements, data pre-processing, exploratory data analysis (EDA), data treatment, modelling, and assessment are all part of the development process. It is recommended to use a Sankey chart to examine patterns based on delayed monthly patterns. The result of this study shows that the largest percentage (71%) of the clients who do not have default payments in the coming month are those who are using revolving credit, paying duly, or those who do not consume the credit card. The usability assessment yielded a 4.0 mean on a five-point Likert scale which indicates that respondents agree that the dashboard is usable. In future development, a prediction model will be created and included into the default payment indicator.

Author Biographies

Siti Salwa Salleh, Faculty of Computer and Mathematical Sciences, University Technology MARA (UiTM), Seremban Campus, Negeri Sembilan, Malaysia

Senior Lecturer, Computer Science Department, Faculty of Computer and Mathematical Sciences, University Technology MARA (UiTM), Seremban Campus, Negeri Sembilan, Malaysia

Nor Ayunie Mohamed, Faculty of Computer and Mathematical Sciences, University Technology MARA, Seremban Campus, Negeri Sembilan, Malaysia

Final year student in Bachelor of Sc (Hons) Management Mathematics

Nur Ainin Sofea Arman Shah, Faculty of Computer and Mathematical Sciences, University Technology MARA, Seremban Campus, Negeri Sembilan, Malaysia

Final year student in Bachelor of Sc (Hons) Management Mathematics

References

A. S. Khalid, N. H. Hassan, N. A. A. B. Razak and A. F. Baharuden, Business intelligence dashboard for driver performance, in fleet management, International Conference on E-Education, E-Business, E-Management, and E-Learning, Osaka, Japan, 2020, 347-351.

S. Few, Information Dashboard Design: Effective Visual Communication of Data, O’Really Media, 2006.

J. Cai, X. Liu and Y. Wu, SVM learning for default prediction of credit card under differential privacy, Workshop on Privacy-Preserving Machine Learning in Practice, 2020, 3 pages.

C. J. Costa and M. Aparicio, Supporting the decision on dashboard design Charts, Proceeding of 254th IIER International Conference, Saint Petersburg, Russia, 2019, 10-15.

W. W. Eckerson, Performance Dashboards. Measuring, Monitoring, and Managing Your Business, Hoboken: Wiley, 2011.

R. K. Rainer and C. G. Cegielski, Introduction to Information Systems. Supporting and Transforming Business, Hoboken: Wiley, 2011.

A. Janes, A. Sillitti and G. Succi, Effective dashboard design, Cutter IT Journal, 26(1), 2013, 17-24.

S. Sharma and V. Mehra, Default payment analysis of credit card clients, 2018, 7 pages, DOI: 10.13140/RG.2.2.31307.28967.

M. M. Hassan and T. Mirza, Credit card default prediction using artificial neural networks, GIS Science Journal, 7, 2020, 383-390.

Y. Ma, Prediction of default probability of credit-card bills, Open Journal of Business and Management, 8, 2020, 231-244.

R. L. Liu, Machine learning approaches to predict default of credit card clients. Modern Economy, 9, 2018, 1828-1838.

J. Zhou, W. Li, J. Wang, S. Ding and C. Xia, Default prediction in P2P lending from high-dimensional data based on machine learning, Physica A: Statistical Mechanics and its Applications, 534, 2019, 122370.

Y. Xia, L. He, Y. Li, N. Liu and Y. Ding, Predicting loan default in peer-to-peer lending using narrative data, Journal of Forecasting, 39(2), 2020, 260-280.

T. M. Alam, K. Shaukat, I. A. Hameed, S. Luo, M. U. Sarwar, S. Shabbir, J. Li and M. Khushi, An investigation of credit card default prediction in the imbalanced datasets, IEEE Access, 8, 2020, 201173-201197.

O. J. Leong and M. Jayabalan, A comparative study on credit card default risk predictive model, Journal of Computer Theoretical Nanoscience, 16(8), 2019, 3591-3595.

A. Hasbun, An Empirical Investigation: Do Animated Graphs Improve the Quality of Sales Forecasting Decisions in Comparison to Tables? Master’s Thesis, Hanken School of Economics, 2009.

K. D. Lawrence, S. Kudyba and R. K. Klimberg, Data Mining Methods and Applications, CRC Press, 2008.

W. Eckerson and M. Hammond, Visual Reporting and Analysis: Seeing Is Knowing, TDWI-The Data Warehousing Institute, 2011, 1-24.

L. Pappas and L. Whitman, Riding the technology wave: effective dashboard data visualization, in Human Interface and the Management of Information, M. J. Smith and G. Salvendy (eds), Lecture Notes in Computer Science, 6771011, 249-258.

A. Chaudhuri, A Visual Technique to Analyze Flow of Information in a Machine Learning System, Walmart Labs, USA, 2019.

A. Lamer, G. Laurent, S. Pelayo, M. E. Amrani, E. Chazard and R. Marcilly, Exploring patient path through sankey diagram: A proof of concept, Studies in Health Technology and Informatics, 270, 2020, 218-222.

V. R. Basili, M. Lindvall, M. Regardie, C. Seaman, J. Heidrich, J. Munch, D. Rombach and A. Trendowicz, Linking software development and business strategy through measurement, Computer, 43(4), 2010, 57-65.

S. Mazumdar, A. Varga, V. Lanfranchi, D. Petrelli and F. Ciravegna, A knowledge dashboard for manufacturing industries, The Semantic Web: ESWC 2011 Workshops, Lecture Notes in Computer Science, 7117, 2012, 112-124.

J. Li, J. Liu, W. Xu and Y. Shi, Support vector machines approach to credit assessment, International Conference on Computational Science, Lecture Notes in Computer Science, 3039, 2004, 892-899.

K. Bache and M. Lichman, UCI Machine Learning Repository, University of California, School of Information and Computer Science, 2013.

I. -C. Yeh and C. -H. Lien, The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients, Expert Systems with Applications, 36(2), 2009, 2473-2480.

S. Guberman, On Gestalt theory principles, Gestalt Theory, 37(1), 2015, 25-44.

A. L. Fruhling and S. Lee, Assessing the reliability, validity and adaptability of PSSUQ, 11th Americas Conference on Information Systems, Nebraska, USA, 2005, 2394-2402.

Downloads

Published

12-10-2021

How to Cite

Salleh, S. S., Mohamed, N. A., & Arman Shah, N. A. S. (2021). Simulating Data Stories of Clients’ Credit Card Default. Applications of Modelling and Simulation, 5, 184–190. Retrieved from https://www.ojs.arqiipubl.com/index.php/AMS_Journal/article/view/307

Issue

Section

Articles

Similar Articles

1 2 3 > >> 

You may also start an advanced similarity search for this article.