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  | ARTICLES | Am. J. innov. res. appl. sci. Volume 10,  Issue 1, Pages 29-43 (Januray 2020)
Research Article 5


         | Valentinos Katsimperis 1 | and | Athanasios Andrikopoulos 2 |.  Am. J. innov. res. appl. sci. 2020; 10(1):29-43.

          | PDF FULL TEXT |  | Received | December 09, 2020 | | Accepted | January 17, 2020 |  | Published | January 26, 2020 |
 
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American Journal of innovative
Research & Applied Sciences 
ISSN  2429-5396 (Online)
OCLC Number: 920041286
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| JANUARY | VOLUME 10 | N° 1 | 2020 |
| Info-AJIRAS-® Journal ISSN 2429-5396 (Online) / Reference  CIF/15/0289M |
  American Journal of Innovative Research & Applied Sciences
Authors Contact

*Correspondant author and authors Copyright © 2020:

| Valentinos Katsimperis 1 | and | Athanasios Andrikopoulos 2 |


Affiliation.

1.2 University of Patras | Department of Computer Engineering and Informatics | School of Engineering | Patras | Greece|

This article is made freely available as part of this journal's Open Access: ID | Valentinos-Ref.1-ajiras110120  |
ABSTRACT

Background
Making a business decision is a major issue for a business. Specifically, for a bank, which is the backbone of a country’s economy, any incorrect decision may have unpleasant consequences such as capital loss, deterioration in financial figures, market share decline, resource wastage etc. The most important business decision that a bank receives is that of lending its money and. This is, in fact, the main activity for the repayment of borrowed funds with interest, the main income for a bank in conjunction with other income (commissions, insurance, etc.). Objectives: This study initially scopes to find the "best" candidate companies to start a new cooperation with a bank, i.e. businesses that stand out among the others with which they will be compared and meet the required banking criteria. On the merits banks will be able to end up with those businesses that have the least chance of defaulting. Methods: An empirical model of mass business rating will be presented. A two-stage evaluation will be used including the ex-ante exclusion (1st stage) and the use of multicriteria analysis via AHP method (2nd stage). This method saves time, sources and assures stability, credibility and quality results. In the present study, the main problem is divided into smaller problems so that the results that will be obtained are more accurate and in more detail. In this way, it is possible to study and analyze all the individual parameters and the data of each smaller subproject. The methodology followed is fully comprehensive and quantified using appropriate analysis techniques. Results: This analysis will result to a list of mass scored businesses saving time and resources. In advance, a database of checked high rated business for potential use of the banks (special products etc.). The results are stable, based on the ahp method and other techniques that will be presented. Conclusions: The present study can simplify the procedure of business rating. Furthermore, it is a comprehensible and easy executable method for any user to deal with. It can be modified in various ways depending on the strictness of the criteria set by the researcher.
Keywords: AHP, business rating, superdecisions.
*Corresponding author Author & Copyright Author © 2020: | Valentinos Katsimperis |. All Rights Reserved. All articles published in American Journal of Innovative Research
and Applied Sciences are the property of Atlantic Center Research Sciences, and is protected by copyright laws CC-BY. See: http://creativecommons.org/licenses/by-nc/4.0/