Optimizing OLAP Cube for Supporting Business Intelligence and Forecasting in Banking Sector

Document Type : Special Issue on Pragmatic Approaches of Software Engineering for Big Data Analytics, Applications and Development


1 Department of Computer Science & Engineering, Birla Institute of Technology, Mesra, Ranchi, India.

2 Dean & Professor, Waljat College of Applied Sciences, Muscat, Oman.

3 Principal, Department of Computer Science & Engineering, Bhagwan Parshuram Institute of Technology, Rohini, India.


The data stored in data warehouse is used for making strategic decisions by integrating heterogeneous data from multiple sources at a single storage place, where data is used for querying and analysis purposes. With the advancement in the technology, Business Analytics and Business intelligence are being increasingly used in the financial sector for forecasting business decisions. Many On-Line Analytical Processing (OLAP) tools are being largely explored that can contribute to business decision making. Banking operation handles a lot of data as they operate daily. Subsequently, preparing of this tremendous volume of information requires instant and quick tools that can process the information at high processing speeds. Through this research paper, we represent the OLAP cube as one of the tools which can be used for business analysis. A case study of a bank and loan approval process is considered as one of the areas for implementation and analysis of business decisions using business intelligence which can serve as a key factor for increasing intelligence in the banking sector to make reliable business decisions. Higher management can forecast and predict various outcomes from the bank data warehouse using On-Line Analytical Processing technology which provided a multidimensional view of the data. Analysts can make business decisions by analyzing the reports and pattern trends in the graphs. Management can modify existing policies and procedures to increase the growth of the bank and can have a healthy competition with their competitors.


Abdou, H. A., & Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intelligent systems in accounting, finance and management, 18(2-3), 59-88.
Aggarwal, D., Mittal, S., & Bali, V. (2019) Prediction Model for Classifying Students Based on Performance using Machine Learning Techniques. In International Journal of Recent Technology and Engineering (IJRTE), 8(2S7), 496-503.
Agrawal, P., Chaudhary, D., Madaan, V., Zabrovskiy, A., Prodan, R., Kimovski, D., & Timmerer, C. (2020). Automated bank cheque verification using image processing and deep learning methods. Multimedia Tools and Applications, 1-32.
Berson, A., & Smith, S. J. (1997). Data warehousing, data mining, and OLAP. McGraw-Hill, Inc.
Bharathi, V. & Akolkar, M. (2004). Banking Services at the Customers’ Palms – Study with Special Reference to Mobile-Banking, 224-230.
Bhat, G., Lee, J. A., & Ryan, S. G. (2019). Using loan loss indicators by loan type to sharpen the evaluation of banks’ loan loss accruals. Available at SSRN 2490670.
Chandra, E., Girsang, A. S., Hadinata, R., & Isa, S. M. (2018, September). Analysis Students' Graduation Eligibility Using Data Warehouse. In 2018 International Conference on Information Management and Technology (ICIMTech) (pp. 61-64). IEEE.
Chaudhary, D., Agrawal, P., & Madaan, V. (2019, June). Bank Cheque Validation Using Image Processing. In International Conference on Advanced Informatics for Computing Research (pp. 148-159). Springer, Singapore.
Chaudhuri, S., & Dayal, U. (1997). An overview of data warehousing and OLAP technology. ACM Sigmod record, 26(1), 65-74.
Codd, E. F., Codd, S. B., & Salley, C. T. (1993). Providing OLAP (on-line analytical processing) to user-analysts. An IT Mandate. White Paper. Arbor Software Corporation.
Dev, H., & Mishra, S. K. (2011). Design of Data Cubes and Mining for Online Banking System. International Journal of Computer Applications, 30(3).
Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., & Pirahesh, H. (1997). Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data mining and knowledge discovery, 1(1), 29-53.
Gupta, G. (2012). Introduction to Data mining with case studies. PHI Learning Private Ltd.
Gurudatt, C., Ravishankar, B., & Jayathirtha, R. V. (2013). Critical analysis of tangible gains post lean ERP implementation in an Indian SME. In 24th Annual Conference of Production and Operations Management Society (POMS).
Hasan, H., & Hyland, P. (2001). Using OLAP and multidimensional data for decision making. IT Professional, 3(5), 44-50.
Inmon, W. H. (2005). Building the data warehouse. John wiley & sons.
Kaur, H., Agrawal, P., & Dhiman, A. (2012, September). Visualizing clouds on different stages of DWH-an introduction to data warehouse as a service. In 2012 International Conference on Computing Sciences (pp. 356-359). IEEE.
Keeton, W. R., & Morris, C. S. (1987). Why do banks’ loan losses differ. Economic review, 72(5), 3-21.
Kenan, S. (2015). Data warehousing: from OLAP to OLTP.
Konikov, A., Kulikova, E., & Stifeeva, O. (2018). Research of the possibilities of application of the Data Warehouse in the construction area. In MATEC Web of Conferences, 251(p. 03062). EDP Sciences.
Mansmann, S., Neumuth, T., & Scholl, M. H. (2007, September). OLAP technology for business process intelligence: Challenges and solutions. In International Conference on Data Warehousing and Knowledge Discovery (pp. 111-122). Springer, Berlin, Heidelberg.
Mathur, A., & Mathur, N. (2016). Design of OLAP Cube for Banking System of India. In International Journal of Emerging Trends & Technology in Computer Science, 35(3), 154-156.
Mathur, A., & Mathur, N. (2018). A Pragmatic Analysis of OLAP Technology Significance in Banking. Journal Homepage: http://esrjournal. com, 6(3).
Osterfelt, S. (2000). Business Intelligence: The Intelligent Customer. DM REVIEW, 10, 80-80.
Pedersen, T. B., & Jensen, C. S. (2001). Multidimensional database technology. Computer, 34(12), 40-46.
Pérez-Martínez, J. M., Berlanga-Llavori, R., Aramburu-Cabo, M. J., & Pedersen, T. B. (2008). Contextualizing data warehouses with documents. Decision Support Systems, 45(1), 77-94.
Pishchukhin, A. M., & Akhmedyanova, G. F. (2018). Algorithms for synthesizing management solutions based on OLAP-technologies. In Journal of Physics: Conference Series, 1015(4), 1-5.
Purohit, S., & Kulkarni, A. (2011, December). Credit evaluation model of loan proposals for Indian Banks. In 2011 World Congress on Information and Communication Technologies (pp. 868-873). IEEE.
Ravishankar, D. B. at the 2013 POMS Annual Conference May 3 to May 6, 2013 at the Denver Marriott City Center. USA" Multi-Factor Significant Improvements Derived Adopting Yield Analysis In A Typical Indian SME.
Sathnanakrishanan, S. (2005). Information System for Banks. Taxman publication, Pvt. Ltd.
Senn, J. A. (1997). Information technology in business: principles, practices, and opportunities. Prentice Hall PTR.
Singh, S., & Bali, V. (2017). Storage and Retrieval of Software Component using Hadoop and MapReduce. In International Journal of Engineering and Technology, 9(4), 2941-2944.
Thomas, H., & Datta, A. (2001). A conceptual model and algebra for on-line analytical processing in decision support databases. Information Systems Research, 12(1), 83-102.
Tohir, A. S., Kusrini, K., & Sudarmawan, S. (2017, November). On-Line Analytic Processing (OLAP) modeling for graduation data presentation. In 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE) (pp. 132-135). IEEE.
Ubiparipović, B., & Đurković, E. (2011). Application of business intelligence in the banking industry. Management Information System, 6(4), 23-30.