A trust-based bio-inspired approach for credit lending decisions |
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Authors: | Monireh Sadat Mirtalaei Morteza Saberi Omar Khadeer Hussain Behzad Ashjari Farookh Khadeer Hussain |
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Affiliation: | (1) Signals, Images, and Intelligent Systems Laboratory (LISSI/EA 3956), University PARIS-EST-Creteil (UPEC), Senart-FB Institute of Technology, Avenue Pierre Point, 77127 Lieusaint, France |
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Abstract: | Credit scoring computation essentially involves taking into account various financial factors and the previous behavior of
the credit requesting person. There is a strong degree of correlation between the compliance level and the credit score of
a given entity. The concept of trust has been widely used and applied in the existing literature to determine the compliance
level of an entity. However it has not been studied in the context of credit scoring literature. In order to address this
shortcoming, in this paper we propose a six-step bio-inspired methodology for trust-based credit lending decisions by credit
institutions. The proposed methodology makes use of an artificial neural network-based model to classify the (potential) customers
into various categories. To show the applicability and superiority of the proposed algorithm, it is applied to a credit-card
dataset obtained from the UCI repository. Due to the varying spectrum of trust levels, we are able to solve the problem of
binary credit lending decisions. A trust-based credit scoring approach allows the financial institutions to grant credit-based
on the level of trust in potential customers. |
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