Business intelligence implementation in Croatian banking sector.

Pejic Bach, Mirjana ; Strugar, Ivan ; Jakovic, Bozidar 等

Abstract: Business Intelligence is one of the best new, innovative 'weapons' for the firms to gain competitive advantage. In this paper main concentration was on data mining and data warehousing which are among most often used business intelligence tools in banking industry. A survey research has been conducted with aim to examine the present implementation of business intelligence tools in the Croatian banking system.

Key words: Business intelligence, bank industry, data mining, data warehousing, Croatian banks

1. INTRODUCTION

In these days companies are leading no-mercy battle on global market. In that 'battle' the main role have decision makers with their crucial decisions. They need good and accurate 'weapon' In order to gain advantage over their competitors. One of the best new, innovative 'weapons' is Business Intelligence. Brackett (2001) notes that business intelligence is a set of concepts, methods and processes to improve business decisions, using information from multiple source and applying experience and adding assumptions to develop an accurate understanding of business dynamics. Hill and Terri (2004) in their study concludes that business intelligence will likely lead to higher level of efficiency, higher and better quality outputs, better marketing decisions and lessened risk of business failure. But expectations of BI systems should be realistic (Wu, 2001). Chou and Tripuramallu (2005) notes that business intelligence software gains more acceptance as users at all levels of the organizations realize all benefits of its decision support capabilities and integrated with enterprise resource planning can greatly improve the IT performance and decision-making capability. Herschel and Jones (2005) are saying that in the future the effectiveness of a business intelligence will be measured based on how well it: (1) promotes and enhances knowledge, (2) improves the mental model(s) and understanding of the decision maker(s) (3) improves decision making and hence firm performance.

The main goal of the paper is to investigate the current level of implementation of business intelligence tools in Croatian banks. An efficient business intelligence system could decrease costs, increase quality and efficiency of day-to-day business. Ultimately, this could increase the competitive strengths of Croatian banks and thus increase the stability of the banking system.

The hypotheses of the paper are: (1) most of Croatian banks do not use business intelligence tools, and (2) banks that use business intelligence tools have different characteristics than banks that do not use them.

2. BUSINESS INTELLIGENCE TOOLS

According to experts from SAS company who is leader in providing BI software and services, Business intelligence software must be fully integrated and comprehensive suite for: (1) Reporting, (2) Query & Analysis, (3)OLAP, (4)Integrated analytics, and (5)Visualization. REPORTING--Company must provide fast, simple access to reports and analysis to their decision makers. Every individual decision maker must be able to generate reports on their own quick and easy through familiar interface.

3. BUSINESS INTELLIGENCE TOOLS IN CROATIAN BANKS

A survey research has been conducted with aim to examine the present implementation of business intelligence tools in the Croatian banking system, and the possibility of their improvement for the purpose of lowering operational cost of banks and maintaining the stability of the banking system. Quite number of banks, 23 banks, from the total of 41 banks which operated in Croatia on 31 December 2003 took part in the research. The research was performed in September 2004 using the face-to-face interview method, and respondents were persons from the information technology department who are best acquainted with the application of advanced information technologies. Among advanced information technologies most frequently used (Table 1) are Intranet (86%), data warehousing (62%) and decision support systems (57%). Among advanced information technologies mentioned in the paper, data mining and data warehousing could be considered as business intelligence tools. Data mining is used by 48% of Croatian banks, and data warehousing is used by 62% of banks.

However, only 44% of the banks use both business intelligence tools, which confirm the first hypothesis of the paper, that most of Croatian banks do not use business intelligence tools. It can be expected that larger and more successful banks possess and make better use of their business intelligence tools. In examining this assumption, t-tests of the means difference for characteristics of the banks considering the existence of the project were applied, considering the random sample of banks was available. Banks with business intelligence tools and banks without it are compared by the following characteristics: total assets, share in total assets, and growth of assets, gross off-balance items, profit (loss) before taxes, capital adequacy rate, and equity (Table 2).

T-test is performed to discover if there is a statistically important difference between mean characteristics of the banks in relation with a usage of business intelligence tools. It appears that banks which use business intelligence tools have larger average total assets (p-value=0.030934), a larger average share in the total assets of the bank market (p-value =0. 030934), larger average gross off-balance items (p-value =0.030934), larger average income (p-value =0.048209), and larger average capital stock, but have a lower average capital adequacy rate (p-value =0.046956).

4. DISCUSSION OF THE RESULTS

Banks that use business intelligence tools own a larger amount of total assets, which illustrates their size because total assets of a bank are expressed in their balance sheet, and show the investment of the financial resources of the bank.

Banks which use business intelligence tools do business with a somewhat lower risk than the other group of banks.

T-test is performed to discover if there is a statistically important difference between mean characteristics of the banks in relation with a usage of business intelligence tools. It appears that banks which use business intelligence tools have larger average total assets (p-value=0.030934), a larger average share in the total assets of the bank market (p-value =0. 030934), larger average gross off-balance items (p-value =0.030934), larger average income (p-value =0.048209), and larger average capital stock, but have a lower average capital adequacy rate (p-value =0.046956). This confirms the second hypothesis of the paper that banks which use business intelligence tools have different characteristics from the banks that do no use them.

5. CONCLUSION

The goal of the paper was to explore usage of business intelligence tools in Croatian banks. We examined the possible business tools and their usage in general, and than we explored their possible usage in banks. The survey on usage of business intelligence tools in Croatian banks was conducted. The results of the survey revealed that only 46% of Croatian banks use both main business intelligence tools (data mining and data warehousing). Banks which use business intelligence tools differ from the banks which do not have such a system. They differ in the following characteristics: size of total assets, participation of their own assets in the Croatian banking sector, size of off-balance items, size of income and capital stock and rate of capital adequacy. In other words, project results shows that banks which use business intelligence tools are larger and more successful.

In this we can see the results of positive feedback growth. Large and successful banks invest more in information technology, especially business intelligence in the purpose of more efficient business reporting. By using business intelligence tools, these banks will use their organizational knowledge even better, and consequently they will become even more successful. This will make possible to invest even more into advanced information technology. In the future research we shall focus on purposes of usage of business intelligence tools.

6. REFERENCES

Brackett, M.H. (2001). Business intellignce value chain, DM Review, pp.1-6

Chou, D.C., Tripuramallu H.B. (2005). BI and ERP integration. Infromation management & Computer Security, Vol. 13 No. 5, pp. 340-349

Gargano, M. L., Raggad, B. G. (1999). Data mining--a powerful information creating tool. OCLC Systems & Services; Volume: 15 Issue: 2

Herschel, R.T.,Jones, N.E. (2005). Knowledge management and business intelligence: the importance of integration. Journal of knowledge management, VOL. 9 NO. 4 2005

Hill, J., Scott, T. (2004). A consideration of the roles of business intelligence and e-business in management and market decision making in knowledge-based and high-tech start-ups. Qualitative Market Research: An International Journal Volume 7--Number 1, pp. 48-57

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Wu, J. (2001), Business intelligence: forecasting success through managing expectations. DM Review, pp. 1-4

Table 1. Usage of advanced information technologies in Croatian banks

 Planned
Technology Used Not used for use

Intranet 86% 10% 4%
Data mining 48% 43% 9%
Data warehousing 62% 19% 19%
Knowledge based systems 14% 86% 0%
Decision support systems 57% 33% 10%
Group decision support systems 19% 71% 9%
Customer resource management 10% 71% 19%
Business process modeling 29% 61% 9%

Table 2. Comparison of bank characteristics in relation with the use
of business intelligence tools

 Average of Average of
 the banks the banks
 that do not that use BI
 use BI tools tools t-value df p-value

Total assets 1073375 12720784 -2,313 21 0,031 *
(000 kn)

Share in the 1% 6% -2,313 21 0,031 *
total assets

Growth of 13% 26% -1,114 21 0,277
assets

Gross 97592 1835644 -2,254 21 0,035 *
off-balance
items (000 kn)

Profit (loss) 11431 188623 -2,097 21 0,049 *
before taxes
(000 kn)

Capital 26 28 -0,215 21 0,832
adequacy rate

Capital stock 126262 920047 -2,110 21 0,0467 *
(000 [kn.sup.1])

* Statistically significant with the Type I. Error of 5%