Абай атындағы ҚазҰпу-нің хабаршысы, «Физика-математика ғылымдары» сериясы, №3 (7 9 ), 2022 150 мрнти
Collection of Necessary Data and Initial Processing
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вестник КазНПУ 2
| 2 Collection of Necessary Data and Initial Processing
The advances in technology have allowed money lenders to reduce credit risk by using all sorts of customer data. By applying statistical and machine learning techniques, cheap data is analyzed and reduced to a single value, known as a credit score, representing credit risk. This sense has the ability to assist in making a conclusion. The higher the credit rating, the more the lender is able to be in no doubt about the creditworthiness of the buyer. Credit scoring is an artificial intelligence configuration based on predictive modeling that considers the possibility that the customer will actually default on a loan promise, be delinquent or insolvent. The prediction model is “trained” by applying historical customer data along with peer group and other data to predict the possibility that a given customer will actually exhibit a particular behavior in the future [2]. The biggest advantage of credit scoring is the ability to quickly and effectively make decisions, for example, to accept or reject a buyer, or to increase or decrease the loan price, interest rate or term. As a result, the speed and accuracy of these judgments have made the credit rating the cornerstone of risk management across all sectors, spanning banking, telecommunications, insurance and retail. Credit scoring can be used throughout the entire customer interaction cycle, including the duration of the customer interaction throughout the relationship between the customer and the organization. But they are primarily intended for credit risk departments, marketing departments still have every chance to benefit from credit scoring methods in their own advertising campaigns [3]. When evaluating orders, the risk of non-compliance with promises by fresh bidders is assessed when deciding whether to accept or reject orders. Behavioral evaluation considers the risk of default associated with an existing customer when making judgments regarding account management such as credit limit, overlimit management, fresh produce. Collection estimation is used in loan collection strategies to estimate the likelihood that buyers who dispose of the pledged asset will repay the obligation. The development of a scientific and competent method and the prevention of individual proposals in this assessment using a credit rating system can be an effective step towards the optimal distribution of collected funds and the reduction of deferred receivables and, consequently, to improve the efficiency of the banking system. Satisfactorily, credit scoring models increase the efficiency of credit solutions in the production of services and meeting the needs of customers and will also be able to reduce the causes of material needs and default of borrowers. Therefore, it is necessary to develop a calming tool for measuring the credit risk of its customers, and for this tool it is necessary to do qualitative and quantitative credit risk using the credit scoring method. The exchange of information on the characteristics of loans for loans and the proportion of their debt can have a significant impact on the effectiveness of credit markets. - Firstly, the exchange of information and knowledge improves the bank's understanding of the characteristics of applicants for benefits and gives more accurate forecasts of the relative probability of placement. - Secondly, according to the fundamentals of credit alienation of banks, the interest rate on their credit resources may fluctuate depending on the level of risk of applicants to a certain extent with the strengthening of monetary and tax policy. - Thirdly, this system can be used to create loan recipients, and, after that, the indicator can reduce the motivation of bank customers to receive additional services and exceed their capabilities by placing various banks and modeling their own condition (without expressing and declaring the real balance of their own received funds) [4]. Абай атындағы ҚазҰПУ-нің ХАБАРШЫСЫ, «Физика-математика ғылымдары» сериясы, № 3 (7 9 ), 2022 152 жүктеу/скачать 1,21 Mb. Достарыңызбен бөлісу:
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