2021
DOI: 10.1088/1742-6596/1998/1/012027
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Credit Score Prediction System using Deep Learning and K-Means Algorithms

Abstract: In financial markets, credit rating and risk assessment tools are used to minimize potential risk up to some extent for credit score. Nowadays, the banking and financial industry has experienced rapid expansion. Therefore, with this growth, the numbers of credit card applications with various credit products are increasing day by day because many people want to avail these services for their personal interest. The challenge here is to identify insights on the performance of a finance industry by using deep lea… Show more

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Cited by 7 publications
(4 citation statements)
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“…Therefore, the approach in this paper and the previous ones largely utilize an offline algorithm while this paper claims an online approach that has large adaptive qualities as shown in Figure 11d and Figure 11c. Meanwhile, works such as [47] utilize k-means and deep learning algorithms to create a default probability to predict credit default, which as mentioned previously is not viable due to the complexity, scarcity and heterogeneity of the available data, similar to real world scenarios.…”
Section: A Additional Related Workmentioning
confidence: 99%
“…Therefore, the approach in this paper and the previous ones largely utilize an offline algorithm while this paper claims an online approach that has large adaptive qualities as shown in Figure 11d and Figure 11c. Meanwhile, works such as [47] utilize k-means and deep learning algorithms to create a default probability to predict credit default, which as mentioned previously is not viable due to the complexity, scarcity and heterogeneity of the available data, similar to real world scenarios.…”
Section: A Additional Related Workmentioning
confidence: 99%
“…Banks as social accountants, screening for credit allocation, the moral and technical aspects for assessing creditworthiness are widely discussed in the literature [24,25,26]. Technically, placing the TCAP in the process undertaken by FIs for credit allocation, we assume a two-stage credit rating system [27]. The first phase computes the probability of default to predict if the beneficiary is a non-defaulter considering indebtedness and credit history.…”
Section: Do Decision Making In Fismentioning
confidence: 99%
“…The following results for 𝑡 = 1 were obtained in negligible computer time by solving the model in (3) through (6). The subset of credit applications (with 𝑥 , , = 1) selected by the model were {1, 2, 3, 5, 10, 15,16,17,18,21,27,29,32,35,37,38,39, 40} making a portfolio return of 40.27% using $1,998,042 of the budget. The impact on the key economic objectives for this industry at period 1 is: net profit $951,595; labor $30,549; domestic supplier $98,586; non domestic supplier $26,237.…”
Section: mentioning
confidence: 99%
“…A wide range of papers applied machine learning such as neural networks, support vector machine, logistic regression, and genetic programming to develop credit scoring models (Louzada et al, 2016). Besides, some papers applied hybrid credit scoring models, such as Munkhdalai et al (2020) used a hybrid credit scoring model with neural networks and logistic regression whereas Kumar et al (2021) used a hybrid credit scoring model with neural networks and k-means algorithm.…”
Section: Introductionmentioning
confidence: 99%