The role of artificial intelligence in developing a banking risk index: an application of Adaptive Neural Network-Based Fuzzy Inference System (ANFIS)

[1]  Yassine Himeur,et al.  AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives , 2022, Artificial Intelligence Review.

[2]  Mani Bansal,et al.  Investigating the disclosure compliance of Basel III in emerging markets: a comparative study between UAE and Indian banks , 2022, Journal of Financial Regulation and Compliance.

[3]  M. H. Hasan,et al.  Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey , 2022, Artificial Intelligence Review.

[4]  A. Kar,et al.  Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective , 2022, Intell. Syst. Account. Finance Manag..

[5]  Ting-Yu Chen.  Decision support modeling for multiple criteria assessments using a likelihood-based consensus ranking method under Pythagorean fuzzy uncertainty , 2022, Artificial Intelligence Review.

[6]  J. Suri,et al.  Human activity recognition in artificial intelligence framework: a narrative review , 2022, Artificial Intelligence Review.

[7]  Abdel-Badeeh M. Salem,et al.  Bankruptcy Prediction Using Artificial Intelligence Techniques: A Survey , 2021, Digital Transformation Technology.

[8]  Deepak Tandon,et al.  Predicting Financial Distress in the Indian Banking Sector: A Comparative Study Between the Logistic Regression, LDA and ANN Models , 2021, Global Business Review.

[9]  S. Valcic.  Utilisation of ANFIS in analysing impact of cost structure on Croatian maritime companies development , 2021, Pomorstvo.

[10]  Sami Ben Jabeur,et al.  CatBoost model and artificial intelligence techniques for corporate failure prediction , 2021 .

[11]  Johannes Lahann,et al.  A systematic literature review on state-of-the-art deep learning methods for process prediction , 2021, Artificial Intelligence Review.

[12]  Hanan Hussain,et al.  Impact of corporate performance on stock price predictions in the UAE markets: Neuro-fuzzy model , 2021, Intell. Syst. Account. Finance Manag..

[13]  R. Chou,et al.  Bank diversification and systemic risk , 2020, The Quarterly Review of Economics and Finance.

[14]  Golnoosh Babaei,et al.  A neural-network-based decision-making model in the peer-to-peer lending market , 2020, Intell. Syst. Account. Finance Manag..

[15]  Herman Karamoy,et al.  The Impact of Banking Risk on Regional Development Banks in Indonesia , 2020, Banks and Bank Systems.

[16]  Andrés Ramírez Hassan,et al.  Inefficiency and bank failure: A joint Bayesian estimation method of stochastic frontier and hazards models , 2020 .

[17]  Yazan F. Roumani,et al.  Predicting firm failure in the software industry , 2019, Artificial Intelligence Review.

[18]  Yevgeniy Bodyanskiy,et al.  A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting , 2019, Data.

[19]  Ahmed A. El-Masry,et al.  Determinants of Capital Adequacy Ratio (CAR) in MENA Region: Islamic vs. Conventional Banks , 2019, International Journal of Accounting and Financial Reporting.

[20]  Xiao-Ping Steven Zhang,et al.  A Budding Romance: Finance and AI , 2018, IEEE MultiMedia.

[21]  Vinod Sharma,et al.  A review on the applications of neuro-fuzzy systems in business , 2018, Artificial Intelligence Review.

[22]  D. K. Sambariya,et al.  Selection of Membership Functions Based on Fuzzy Rules to Design an Efficient Power System Stabilizer , 2017, Int. J. Fuzzy Syst..

[23]  C. Robert.  Superintelligence: Paths, Dangers, Strategies , 2017 .

[24]  Maosen Cao,et al.  Advanced Methods in Neural Networks-Based Sensitivity Analysis with their Applications in Civil Engineering , 2016 .

[25]  Chung-Ho Su,et al.  A hybrid fuzzy time series model based on ANFIS and integrated nonlinear feature selection method for forecasting stock , 2016, Neurocomputing.

[26]  S. Saksonova.  The Role of Net Interest Margin in Improving Banks’ Asset Structure and Assessing the Stability and Efficiency of their Operations , 2014 .

[27]  Samir Srairi.  Ownership structure and risk-taking behaviour in conventional and Islamic banks: Evidence for MENA countries , 2013 .

[28]  Mansi Subhedar,et al.  Comparison of Mamdani and Sugeno Inference Systems for Dynamic Spectrum Allocation in Cognitive Radio Networks , 2013, Wirel. Pers. Commun..

[29]  Richard Apostolik,et al.  Foundations of Banking Risk: An Overview of Banking, Banking Risks, and Risk-Based Banking Regulation , 2012 .

[30]  Pingzhou Cao,et al.  Factor Sensitivity Analysis with Neural Network Simulation based on Perturbation System , 2011, J. Comput..

[31]  Ritu Tiwari,et al.  Fuzzy Inference Systems , 2010 .

[32]  N. Georganas,et al.  A comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of Hapto-Audio-Visual applications , 2008, 2008 IEEE International Workshop on Haptic Audio visual Environments and Games.

[33]  S. L. Semiatin,et al.  Determination of the beta-approach curve and beta-transus temperature for titanium alloys using sensitivity analysis of a trained neural network , 2006 .

[34]  Beth A Sproule,et al.  Fuzzy pharmacology: theory and applications. , 2002, Trends in pharmacological sciences.

[35]  Derek Partridge,et al.  Assessing the Impact of Input Features in a Feedforward Neural Network , 2000, Neural Computing & Applications.

[36]  Sushmita Mitra,et al.  Neuro-fuzzy rule generation: survey in soft computing framework , 2000, IEEE Trans. Neural Networks Learn. Syst..

[37]  Daniel S. Yeung,et al.  Sensitivity analysis of neocognitron , 1999, IEEE Trans. Syst. Man Cybern. Part C.

[38]  Lakhmi C. Jain,et al.  Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications , 1998 .

[39]  Robin Pitblado,et al.  Next generation risk management , 1997 .

[40]  Tamás D. Gedeon,et al.  Data Mining of Inputs: Analysing Magnitude and Functional Measures , 1997, Int. J. Neural Syst..

[41]  D. Lamy,et al.  Modeling and sensitivity analysis of neural networks , 1996 .

[42]  Yannis Dimopoulos,et al.  Use of some sensitivity criteria for choosing networks with good generalization ability , 1995, Neural Processing Letters.

[43]  Hao Ying,et al.  Essentials of fuzzy modeling and control , 1995 .

[44]  J. Simonoff,et al.  Procedures for the Identification of Multiple Outliers in Linear Models , 1993 .

[45]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[46]  Mohamed El Merouani,et al.  Predicting financial distress using hybrid feedforward neural network with cuckoo search algorithm , 2020, ANT/EDI40.

[47]  Said Marso,et al.  Bankruptcy Prediction using Hybrid Neural Networks with Artificial Bee Colony , 2020 .

[48]  Manoranjan Pal,et al.  Applications of Regression Techniques , 2019 .

[49]  M. Pal,et al.  Introduction to Correlation and Linear Regression Analysis , 2019, Applications of Regression Techniques.

[50]  Petr Hájek,et al.  Interval-valued fuzzy cognitive maps with genetic learning for predicting corporate financial distress , 2018 .

[51]  Bart van Liebergen,et al.  Machine learning: A revolution in risk management and compliance? , 2017 .

[52]  Pawel Lewicki,et al.  Statistics : methods and applications : a comprehensive reference for science, industry, and data mining , 2006 .

[53]  Daniel S. Yeung,et al.  A Quantified Sensitivity Measure for Multilayer Perceptron to Input Perturbation , 2003, Neural Computation.

[54]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[55]  Lotfi A. Zadeh,et al.  Fuzzy Logic Toolbox User''''s Guide , 1995 .

[56]  菅野 道夫,et al.  Applied fuzzy systems , 1994 .

[57]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .