In the 21st century, the technological revolution has changed the human activity, including in banking sector. Bank managers are using all IT inovations, including expert systems,, in order to reduce the risks of human mistakes.
There are several groups of expert systems for finance according to the problem they treat:
- Expert systems for financial analysis of firms (Smith & McDuffie 1996; Matsatsinis, Doumpos & Zopounidis, 1997; O'Leary, 1995). An advantage of expert systems is that besides quantitative ratios such as profitability, total profit, short term and long term debt and other, they can treat qualitative ratios also: position at the market, organization of personnel, business reputation, marketing flexibility, etc. Successful financial analysis determines the firm’s status, that is, a level of risks for a possible investment or a credit approval.
- Expert systems for analysing the causes of successful or unsuccessful business development (Apte 1989; Pinson, 1992; Ruparel & Srinivasan, 1992; Chinn & Madey, 1997). Because of the possibility to draw a conclusion about a certain business development or a specific project by looking backward or forward, and also the ability to follow data that can change over time, it is possible to find causes of the temporary state of a fairs in the business/project and predict its future.
- Expert systems for market analysis (Chan, Dillon & Saw 1989; Dhananjayan, Raman, & Sarukesi 1989; Smith, McDuffie & Flory 1991). If a product has already been produced before by a company, then its sale can be analysed by an expert system. The system can take into account different factors that can possibly decrease the sale (high price, low quality, bad commercial, stiff competition, etc.), and then on the bases of the analysis the company should decide on further steps (to improve quality, to improve production, or to start producing a new product).
- Expert systems for acquiring knowledge in a subfield of finance (Boer & Livnat 1990; Brown & Wensley 1995; Hartvigsen 1992). These expert systems are widely used in educating managers and other financial experts. Besides, this kind of knowledge can advance and be improved rapidly, so the knowledge bases of such expert systems are suitable means for its conservation, improvement and reusability.
1. Fineva – an expert system for financial analysis of companies
The complete methodology for knowledge acquisition and representation in the field of financial analysis is implemented in the system called FINEVA (FINancial EVAluation) (Matsatsinis, Doumpos & Zopounidis 1997). The FINEVA system is a multicriteria knowledge-based decision support system for the assessment of corporate performance and viability. The system has been developed using the M4 expert system shell, by N.F. Matsatsinis, M. Doumpos and C. Zopounidis of Technical University on Crete.
Financial analysis of firms involves identification of the strengths and weaknesses of firms, mainly through judgemental procedures concerning the qualitative evaluation and interpretation of financial ratios. The technology of expert systems (ES-s) technology is well suited to these kinds of tasks. The symbolic reasoning of ES-s enables them not only to make conclusions, through a process similar to the one used by human experts, but also to provide explanations concerning their estimations.
2. Port-Man – an expert system for bank management
One of the major areas of service provided by the banking industry is to help people to plan the financial aspect of their lives. In order to function effectively, banks must be able to advise their customers on the best possible arrangement that would suit their individual investment needs. This implies that the investment advisor must have knowledge of the products offered by the bank and the ability to recognize the customer’s needs and match these needs with the appropriate products.
Currently this service is performed by the bank officers. One of the problems is the non-consistency of the advice given from these officers. Certain products could be well-known to some officers but are ignored by others. Hence, the same situation could lead to different advice from different bank officers. Furthermore, the consultation process is usually complicated and may take some time. This delay could lead to the investor’s impatience with the process, with the likely loss of the client to the bank. Hence an expert system can greatly improve a bank’s service to its customer, as it could make this service more readily available and greatly speed up the process. Port-Man expert system has been developed by Y. Y. Chan, T. S. Dillon and E. G. Saw at the La Trobe University in Bundoora, Australia (Chan, Dillon. & Saw 1989). Port-Man is a banking advisory system designed to assist bank officers to give advice on personal investment in a bank. It helps to speed up the consultation process and standardize the experience of the bank’s financial consultants. The task of the system is to select a range of bank products that will satisfy the criteria for investment. The selected products are ranked according to the rates of return on-investment and risk levels. Moreover, various side-effects for the investor, such as tax variation or pension adjustment, will be taken into consideration. Upon request, the system will give an explanation of how a product is selected. In addition, the user may query the system during the consultation process. Finally, Port-Man allows the user to change any previous input or investment criteria, and the system will then restart the process at the appropriate stage.
We can say that these new changes might influence the banking industry in the future, reducing the role of human activity in decision making process. The industry will need less workers, but on the other side, the customers will benefit, because the banking services will be more accessible.