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In order to pay back the principal borrowed from the depositary bank, the interest collected by principal creditors will be collected. In this paper, we have presented the main classifiers which are used in credit evaluation. From the research, we have noticed that there are some classifiers who find application in the credit allocation decision. Credit risk assessment is becoming a critical field of financial risk management. Many approaches are used for the credit risk evaluation of client data sets. The evaluation of credit risk data sets leads to an option of cancelling the loan or refusing the request of the borrower, which requires a detailed examination of the data set or of the customer’s data. This paper discusses various automatic methods of credit risk analysis used for the estimation of credit risk. The data mining method was defined with the emphasis on different algorithms, such as neural network, and as the most widely employed approach for credit risk analysis.

This article includes a developed model and well-defined process that one should undertake in order to contribute in the prediction of the potential stock price fluctuation solely based on financial news from relevant sources. We are providing background information on this topic adding the role of text mining in general, furthermore supporting the idea with the study of relevant research articles to narrow the focus on the problemwe are researching.Our proposedmodel relies on existing text-mining techniques used for sentiment analysis, combinedwith historical data from relevant news sources as well as stock data. In confirming the model, after the experiment we have provided the results of the simulation, which are opening the ground for further explorations in this sensitive area of prediction.