A Weights Direct Determination Neural Network for Credit Card Attrition Analysis

Yang, Y., Wu, P., Katsikis, V. N., Li, S., & Feng, W. (2025). A novel real-time noise-resilient zeroing neural network and its applications to matrix problem solving. Mathematics and Computers in Simulation. Website Abstract

Given the critical role of zeroing neural networks (ZNN) in various fields and the practical demand for models in effectively resisting real-time noise, this study introduces a novel anti-noise integral zeroing neural network (AN-IZNN) model alongside its enhanced counterpart (EAN-IZNN), for the applications of matrix problem solving. Theoretical analysis demonstrates their ability to achieve convergence even under different noise conditions. Both theoretical analyses and simulation validations highlight the superior performance of the proposed models over existing neural network models. Notably, the root mean square error of the proposed AN-IZNN and EAN-IZNN models is reduced by 92.6249% and 91.4178%, respectively, compared to scenarios without the proposed method, demonstrating the effectiveness of the solution.

Cao, X., Yang, Y., Li, S., Stanimirović, P. S., & Katsikis, V. N. (2025). Artificial Neural Dynamics for Portfolio Allocation: An Optimization Perspective. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1-12.

Katsikis, V. N., Mourtas, S. D., Sahas, R., & Balios, D. (2024). A Weights Direct Determination Neural Network for Credit Card Attrition Analysis. In L. A. Maglaras, Das, S., Tripathy, N., & Patnaik, S. (Eds.), Machine Learning Approaches in Financial Analytics (pp. 325–346). Cham: Springer Nature Switzerland. Website Abstract

Cost reduction is a component that contributes to both the profitability and longevity of a corporation, especially in the case of a financial institution, and can be accomplished through greater client retention. Particularly, credit card customers comprise a volatile subset of a bank's client base. As such, banks would like to predict in advance which of those clients are likely to attrite, so as to approach them with proactive marketing campaigns. Credit card attrition is generally a poorly investigated subtopic with a variety of challenges, like highly imbalanced datasets. This article utilizes neural networks to address the challenges of credit card attrition since they have found great application in many classification problems. More particularly, to overcome the shortcomings of traditional back propagation neural networks, we construct a multi-input trigonometrically activated weights and structure determination (MTA-WASD) neural network which incorporates structure trimming as well as other techniques that boost its training speed as well as diminish the danger and the subsequent detrimental effects of overfitting. When applied to three publicly available datasets, the MTA-WASD neural network demonstrated either superior or highly competitive performance across all metrics, compared to some of the best-performing classification models that MATLAB's classification learner app offers.

Cao, X., Li, S., Katsikis, V., Khan, A. T., He, H., Liu, Z., Zhang, L., et al. (2024). Empowering financial futures: Large language models in the modern financial landscape. EAI Endorsed Transactions on AI and Robotics, 3. presented at the Jul. Website

Stanimirović, P. S., Mourtas, S. D., Mosić, D., Katsikis, V. N., Cao, X., & Li, S. (2024). A Zeroing Neural Network Approach for Calculating Time-Varying G-Outer Inverse of Arbitrary Matrix. IEEE Transactions on Neural Networks and Learning Systems, 1-10.

Balios, D., Katsikis, V. N., Naoum, V. C., & Zaroulea, T. (2024). The effect of industry level characteristics and cross-country differences on earnings management: A European comparative perspective. Journal of Governance & Regulation, 13(2), 403-418. Publisher's Version

Jerbi, H., Al-Darraji, I., Albadran, S., Aoun, S. B., Simos, T. E., Mourtas, S. D., & Katsikis, V. N. (2024). Solving quaternion nonsymmetric algebraic Riccati equations through zeroing neural networks. AIMS Mathematics, 9, 5794-5809. Website

Mourtas, S. D., Katsikis, V. N., Stanimirović, P. S., & Kazakovtsev, L. A. (2024). Credit and Loan Approval Classification Using a Bio-Inspired Neural Network. Biomimetics, 9. Website Abstract

Numerous people are applying for bank loans as a result of the banking industry’s expansion, but because banks only have a certain amount of assets to lend to, they can only do so to a certain number of applicants. Therefore, the banking industry is very interested in finding ways to reduce the risk factor involved in choosing the safe applicant in order to save lots of bank resources. These days, machine learning greatly reduces the amount of work needed to choose the safe applicant. Taking this into account, a novel weights and structure determination (WASD) neural network has been built to meet the aforementioned two challenges of credit approval and loan approval, as well as to handle the unique characteristics of each. Motivated by the observation that WASD neural networks outperform conventional back-propagation neural networks in terms of sluggish training speed and being stuck in local minima, we created a bio-inspired WASD algorithm for binary classification problems (BWASD) for best adapting to the credit or loan approval model by utilizing the metaheuristic beetle antennae search (BAS) algorithm to improve the learning procedure of the WASD algorithm. Theoretical and experimental study demonstrate superior performance and problem adaptability. Furthermore, we provide a complete MATLAB package to support our experiments together with full implementation and extensive installation instructions.

He, Y., Dong, X., Simos, T. E., Mourtas, S. D., Katsikis, V. N., Lagios, D., Zervas, P., et al. (2024). A bio-inspired weights and structure determination neural network for multiclass classification: Applications in occupational classification systems. AIMS Mathematics, 9, 2411-2434. Website

Jerbi, H., Alshammari, O., Aoun, S. B., Kchaou, M., Simos, T. E., Mourtas, S. D., & Katsikis, V. N. (2024). Hermitian Solutions of the Quaternion Algebraic Riccati Equations through Zeroing Neural Networks with Application to Quadrotor Control. Mathematics, 12. Website Abstract

The stability of nonlinear systems in the control domain has been extensively studied using different versions of the algebraic Riccati equation (ARE). This leads to the focus of this work: the search for the time-varying quaternion ARE (TQARE) Hermitian solution. The zeroing neural network (ZNN) method, which has shown significant success at solving time-varying problems, is used to do this. We present a novel ZNN model called ’ZQ-ARE’ that effectively solves the TQARE by finding only Hermitian solutions. The model works quite effectively, as demonstrated by one application to quadrotor control and three simulation tests. Specifically, in three simulation tests, the ZQ-ARE model finds the TQARE Hermitian solution under various initial conditions, and we also demonstrate that the convergence rate of the solution can be adjusted. Furthermore, we show that adapting the ZQ-ARE solution to the state-dependent Riccati equation (SDRE) technique stabilizes a quadrotor’s flight control system faster than the traditional differential-algebraic Riccati equation solution.

Vasilios Katsikis

Professor, Economics

A Weights Direct Determination Neural Network for Credit Card Attrition Analysis

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