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 learning algorithms as they directly affect the viability of that industry. These industries have a limited number of resources and capital, which can be used to deliver the services among the customers. In this research work, we proposed prediction of credit scoring system using deep learning and K-Means algorithm for the financial industry. The scheme contains a predictive model which uses feature selection (FS) classification and deep learning applications simultaneously to train the proposed model to perform effectively. The scheme 1) pre-processing credit card data 2) uses a feature selection technique to minimize the dimension of data in order to obtain the finest training data 3) applies a deep learning algorithm to map the input weight with hidden biases to achieve excellent performance 4) Decision support system is used to enable the deep learning algorithm to provide a more accurate and intelligent decision. Furthermore, the proposed model is validated on different credit scoring dataset in real-world scenarios and is capable of improving the effectiveness and accuracy. The studies indicate that our predictive model performs well for credit scoring of existing customer and helps lenders to allocate funds in finance industry.

A Learning and Control Perspective for Microfinance

“…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.…”

Transdisciplinary Credit Allocation Policy to Foster Regional Economic Development Through Financial Institutions

“…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.…”

“…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.…”

Modeling Tenant’s Credit Scoring Using Logistic Regression

“…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.…”