Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment

Asogbon, Mojisola Grace

doi:10.11648/j.ijiis.20160501.13

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…As a result, the ANN becomes more transparent, while the fuzzy logic system becomes capable of learning. In addition, the NFS can be trained to develop IF-THEN fuzzy rules and determine membership functions (MFs) for input and output variables of the system [17,18]. Among all methods of integrating fuzzy systems and ANNs, a hybrid NFS has the most potential.…”

Section: Related Workmentioning

confidence: 99%

Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model

Atlam

Azad

Fadhel

2022

Sensors

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Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg–Marquardt (LM), Conjugate Gradient with Fletcher–Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children’s hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features.

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Section: Related Workmentioning

confidence: 99%

Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model

Atlam

Azad

Fadhel

2022

Sensors

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“…In [6], the researcher proposed a hybrid system that used neural networks to build a model based on learning abilities and put it into a fuzzy inference module for loan risk evaluation. It is based on an accuracy of forecasting loan risks and measures of average absolute deviation.…”

Section: Literature Reviewmentioning

confidence: 99%

A Proposed Fuzzy Model for Reducing the Risk of Insolvent Loans in the Credit Sector as Applied in Egypt

Hasan¹,

El-Ghareeb²,

Farahat³

et al. 2021

IJFIS

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Non-financial analysis is one of the varied crucial directive tools of credit study that is used for judging whether the client has a genuine desire to pay the assigned amounts of the loan at its maturity dates. Fuzzy logic can help to solve the problem of dealing with factors of non-financial analysis by converting the linguistic variables to numerical variables to calculate their accuracy. This study proposes a fuzzy model that contains a complete database of non-financial factors used by the decision-maker using a fuzzy logic technique, which helps in building the fuzzy rules with great accuracy and helps in predicting the actual situation of the client. In addition, it provides constant following-up of the uses of the granted loan to guarantee that all terms set by the bank are met so that the bank can avoid future defaulting of the client. The proposed model is applied in the credit department of a private Egyptian bank (QNB), with random samples of previous real clients. Some real standards are set to calculate non-financial factors that are related to the client, management, economic situation, and project activity. The results of the proposed model revealed that the correlation factor is 95.3% between real successful payment clients and successful model clients. To guarantee the accuracy of the knowledge base quality and validation, the knowledge model was presented to the credit manager of the bank under study (expert), who provided a full evaluation of the results of the proposed model compared to the actual situation of clients.

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Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment

Cited by 2 publications

References 8 publications

Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model

Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model

A Proposed Fuzzy Model for Reducing the Risk of Insolvent Loans in the Credit Sector as Applied in Egypt

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