AN IT2FS MODEL FOR SHARIA CREDIT SCORING: ANALYSIS &amp; DESIGN

Sidik, Galih Kurniawan; Djatna, Taufik; Buono, Agus

doi:10.21609/jsi.v9i2.352

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…To rate specific banking customers, a new method had been utilized based on fuzzy sets and particular rules in the bank [10]. A method of IT2FS has been used for scoring from intervals between 0 and 100 [11]. Abdou et al presented In demand for the facility from 487 actual data (achieved through the Islamic Bank of England), of which 336 cases have been accepted and 151 cases have been rejected [12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…(1) Existence or nonexistence of account with a check handle (four various modes) (A1) (2) Monthly turnover rate (A2) (3) Credit history (five various modes) (A3) (4) Target (eleven various modes) (A4) (5) e amount of credit received (A5) (6) Savings account and its amount (five various modes) (A6) (7) e Duration of employment by occupation (five various modes) (A7) (8) Installment ratio in return for assets seized by the bank (A8) (9) Gender and marital status (five various modes) (A9) (10) Debt or previous warranty (three various modes) (A10) (11) Residence status (A11) (12) Assets (four various modes) (A12) (13) Age (A13) (14) Other installments (three various modes) (A14) (15) Housing status (three various modes) (A15) (16) e number of credits in the bank (A16) (17) Job status (four various modes) (A17) (18) e number of Guarantors (A18) (19) e availability of landline phones (A19) (20) Foreign employee (A20)…”

Section: Simulation Conditionsmentioning

confidence: 99%

See 1 more Smart Citation

Banks Credit Risk Prediction with Optimized ANN Based on Improved Owl Search Algorithm

Sharifi

Jain

Poshtkohi

et al. 2021

Mathematical Problems in Engineering

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Credit is one of the most significant elements in banks and financial institutions. It can also be described as unpredicted events, which mainly occur in the form of either assets or liabilities. The risk occurrence is that the facility recipients have no willingness and ability to repay their debt to the bank, which is a default that is synonymous with credit risk. Credit ratings are a way to decrease and measure credit risk and, therefore, manage it appropriately. Credit rating is an approach for estimating the features and recipients of facilities’ performance based on quantitative criteria, including the company’s financial information. The anticipated future performance allows the applicants to obtain facilities with the exact specifications. In this study, due to the need and significance of calculating the credit risk concept, a novel method based on the hybrid method of artificial neural networks and an improved version of Owl search algorithm (IOSA) and forecasting of C5 risk of decision tree credit is done. This algorithm has two major parts. The decision tree runs based on an IOSA to provide the best weighting of the neural network. The weights created along with the problem data are then given as the input to the main network, and the data are classified. The algorithm has the highest level of accuracy, 96% that is much higher than other algorithms. The results also show a precision of 0.885 and a recall of 0.83 for 618 true positive samples. The proposed method has the highest accuracy and reliability toward the other comparative methods. The study is based on actual data noticed in one of the branches of the Bank Melli, Iran.

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Section: Literature Reviewmentioning

confidence: 99%