Affiliation:
1. Department of Computer Science, T.K. Govt. Arts College, Vridhachalam, India
2. Tamil Virtual Academy, Chennai,
India
Abstract
Background:
At present, financial Credit Scoring (CS) is considered as one of the hottest
research topics in finance domain, which assists in determining the credit value of individual persons
as well as organizations. Data mining approaches are found to be useful in banking sectors, which
assist them in designing and developing proper products or services to the customer with minimal
risks. Credit risks are linked to loss and loan defaults, which are the main source of risks that exist in
the banking sector.
Aim:
The current research article aims at presenting an effective credit score prediction model for
banking sector which can assist them to foresee the credible customers, who have applied for loan.
Methods:
An optimal Deep Neural Network (DNN)-based framework is employed for credit score
data classification using Stacked Autoencoders (SA). Here, SA is applied to extract the features from
the dataset. These features are then classified using SoftMax layer. Besides, the network is also tuned
Truncated Backpropagation Through Time (TBPTT) model in a supervised way using the training
dataset.
Results:
The proposed model was tested using a benchmark German credit dataset, which includes
the necessary variables to determine the credit score of a loan applicant. The presented SADNN
model achieved the maximum classification while the model attained high accuracy rate of 96.10%,
F-score of 97.25% and kappa value of 90.52%.
Conclusion:
The experimental results pointed out that a maximum classification performance was
attained by the proposed model on all different aspects. The proposed method helped in determining
the capability of a borrower in repaying the loan and computing the credit risks properly.
Publisher
Bentham Science Publishers Ltd.
Cited by
5 articles.
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