Enhancement of a credit rating tool for Company X
Sterkhov, Evgenii (2020)
Sterkhov, Evgenii
2020
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202004175238
https://urn.fi/URN:NBN:fi:amk-202004175238
Tiivistelmä
The main goal of this thesis is to enhance an existing credit rating model in order to prevent fraud and decrease the number of non-paying loans. This thesis is issued by a new non-bank lending company that is seeking to improve its processes and become more profitable.
The theory framework introduces and explains the concepts of small and medium businesses, financial markets and various credit risk analysis techniques. Methods already utilised at the commissioning company were researched to understand if they can be improved further. Bankruptcy prediction models were chosen as an additional method for a more accurate analysis of the financial data. The chosen model was tested by analysing a group of Finnish companies.
This thesis also considers options on how the commissioning company can detect and eliminate fraud. A framework for fraud detection was created, as well as a model for the analysis of qualitative data, such as the digital activity of the company. Every factor was given a "weight" according to the importance of it to the founder of Company X. The created model rates the company based on the non-financial information available online by converting it into quantitative data. This helps the employees at Company X to better understand the company's position and in turn make accurate lending decisions.
The qualitative and quantitative parts of the tool were combined to create a new credit rating model. The results of the testing of the tool show if the additional factors improve the credit risk analysis compared to the previously used framework. Company X representative has found the model to be valuable and has given his feedback.
The theory framework introduces and explains the concepts of small and medium businesses, financial markets and various credit risk analysis techniques. Methods already utilised at the commissioning company were researched to understand if they can be improved further. Bankruptcy prediction models were chosen as an additional method for a more accurate analysis of the financial data. The chosen model was tested by analysing a group of Finnish companies.
This thesis also considers options on how the commissioning company can detect and eliminate fraud. A framework for fraud detection was created, as well as a model for the analysis of qualitative data, such as the digital activity of the company. Every factor was given a "weight" according to the importance of it to the founder of Company X. The created model rates the company based on the non-financial information available online by converting it into quantitative data. This helps the employees at Company X to better understand the company's position and in turn make accurate lending decisions.
The qualitative and quantitative parts of the tool were combined to create a new credit rating model. The results of the testing of the tool show if the additional factors improve the credit risk analysis compared to the previously used framework. Company X representative has found the model to be valuable and has given his feedback.