Machine Learning in Consumer Credit Risk Analysis: A Review
نویسندگان
چکیده
منابع مشابه
Consumer credit risk: Individual probability estimates using machine learning
Consumer credit scoring is often considered a classification task where clients receive either a good or a bad credit status. Default probabilities provide more detailed information about the creditworthiness of consumers, and they are usually estimated by logistic regression. Here, we present a general framework for estimating individual consumer credit risks by use of machine learning methods...
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Credit risk models are used by financial companies to evaluate in advance the insolvency risk caused by credits that enter into default. Many models for credit risk have been developed over the past few decades. In this paper, we focus on those models that can be formulated in terms of the probability of default by using survival analysis techniques. With this objective three different mechanis...
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Consumer credit risk assessment involves the use of risk assessment tools to manage a borrower’s account from the time of pre-screening a potential application through to the management of the account during its life and possible write-off. The riskiness of lending to a credit applicant is usually estimated using a logistic regression model though researchers have considered many other types of...
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The corporate credit risk literature has many studies modelling the change in the credit risk of corporate bonds over time. There is far less analysis of the credit risk for portfolios of consumer loans. However behavioural scores, which are commonly calculated on a monthly basis by most consumer lenders are the analogues of ratings in corporate credit risk. Motivated by studies in corporate cr...
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ژورنال
عنوان ژورنال: International Journal of Advanced Trends in Computer Science and Engineering
سال: 2020
ISSN: 2278-3091
DOI: 10.30534/ijatcse/2020/328942020