ARF: A hybrid model for credit scoring in complex systems

Tezerjan, Mostafa Yousofi; Samghabadi, Azamdokht Safi; Memariani, Azizollah

doi:10.1016/j.eswa.2021.115634

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…Risk calculation and prediction were reported in the literature based on statistical techniques (Micán et al, 2022;Ponsard et al, 2019) and Artificial Intelligence (AI) models (Neumeier et al, 2018;Zhang, 2020). Moreover, neuro-fuzzy models, hybridising ANN and FIS, were also proposed to handle different problems such as the insurance business risk estimation (Hessami, 2018), the overseas construction projects decision prediction (Utama et al, 2019), and stock market segment shocks detection and prediction (Yousofi Tezerjan et al, 2021). However, the latter models were not used to compute the OPR.…”

Section: Introductionmentioning

confidence: 99%

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

Zaidouni,

Janati Idrissi,

Bellabdaoui

2024

JICT

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Risk inherence jeopardises Information System (IS) and Information Technology (IT) Project Portfolio Management (PPM) to realise thestrategic objectives. Previous studies have mainly provided Artificial Intelligence (AI) and statistical models to predict the overall riskof IS/IT project portfolio, whereas neuro-fuzzy models were rarely used. This paper proposes a Sugeno Adaptive Neuro-Fuzzy InferenceSystem (ANFIS) model based on Fuzzy Factor Analysis (FFA) named ANFIS-OPR to predict the overall risk of IS/IT project portfolio fromhistorical IS/IT project risk data. The ANFIS-OPR inputs are the relevant factor loadings resulting from the FFA application on the IS/IT projects risks set to cope with the curse of dimensionality. Then, the Sugeno ANFIS model is adopted to give strategic interpretability to the predicted IS/IT project portfolio overall risk by implementing the IS/IT Project Management Office (PMO) expert knowledge, represented by fuzzy rules, on the relationship between IS/IT project portfolio strategic alignment and the IS/IT projects risks. The ANFISOPR outputs are the predicted Overall Portfolio Risk (OPR) and Root Mean Square Error (RMSE). The paper also presents an IS/IT PMO case study that shows the proposed ANFIS-OPR efficacy, which predicted the OPR values closely to the OPR estimates with an accepted RMSE of 0.108. The proposed ANFIS-OPR is a novel intelligent decision-making tool that enables the IS/IT PMO to monitor the OPR, considering its linkage with strategic alignment; thus, contingency plans can be carried out appropriately while ensuring that the IS/IT project portfolio is strategically aligned.

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Section: Introductionmentioning

confidence: 99%

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

Zaidouni,

Janati Idrissi,

Bellabdaoui

2024

JICT

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“…This type of research method is primarily based on the experience and subjective analysis of bank experts to assess credit risk. In practice, due to the strong subjectivity, the determination of debtor default rates, default factors and their weights relies heavily on the creditors􀆳 own judgment, thus making the assessment results of credit risk lack objectivity and scientificity [6,7] . Following the traditional methods of credit risk analysis, multivariate statistical models have been widely used abroad in the assessment of credit risk, summarized as linear probability models, logistic models, probit models and discriminant analysis models [8] .…”

Section: Introductionmentioning

confidence: 99%

A Novel DBN-EFA-CFA-Based Dimensional Reduation for Credit Risk Measurement

Yue

Huang

Shi

et al. 2023

Wuhan Univ. J. Nat. Sci.

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Affected by the Federal Reserve's interest rate hike and the downward pressure on the domestic economy, the phenomenon of default is still prominent. The credit risk of the listed companies has become a growing concern of the community. In this paper we present a novel credit risk measurement method based on a dimensional reduation technique. The method first extracts the risk measure indexes from the basal financial data via dimensional reduation by using deep belief network (DBN), exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in turn. And then the credit risk is measured by a systemic structural equation model (SEM) and logistic distribution. To validate the proposed method, we employ the financial data of the listed companies from Q1 2019 to Q2 2022. The empirical results show its effectiveness on statistical evaluation, assessment on testing samples and credit risk forecasting.

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Credit Risk Assessment System Based on Deep Learning: A Systematic Literature Review

Hoyos Gutiérrez,

Santos López

2024

Lecture Notes in Networks and Systems

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ARF: A hybrid model for credit scoring in complex systems

Cited by 6 publications

References 30 publications

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

A Novel DBN-EFA-CFA-Based Dimensional Reduation for Credit Risk Measurement

Credit Risk Assessment System Based on Deep Learning: A Systematic Literature Review

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