An Enhanced TSA-MLP Model for Identifying Credit Default Problems

Credit default has always been one of the critical factors in the development of personal credit business. By establishing a default identification model, default can be avoided effectively. There are some existing methods to identify credit default. However, these methods have some problems: Problem (1): It is different to deal the non-linear data, Problem (2): The local stagnation results in the high error rate, and Problem (3): The premature convergence leads to the low classification rate. In this paper, the sinhTSA-MLP default risk identification model is proposed to solve these problems. In this model, the proposed sinhTSA method can effectively avoid the problems of falling into local optimum and premature convergence. And the benchmark test results demonstrate sinhTSA is superior to other methods. According to the two experiments, the classification rate reaches 77.35% and 96.48%. Therefore, the sinhTSA-MLP default identification model has some particular advantages in identifying credit problems The feasibility of the sinhTSA-MLP default identification model has been proved through helping to manage credit default more consciously

Actuarial science · Computer security · Credit risk · Econometrics · Economics · Rate of convergence · Computer Science · Credit Risk and Financial Regulations · Financial Distress and Bankruptcy Prediction · Imbalanced Data Classification Techniques · Artificial Intelligence

• GWO-MLP Based Achievement Prediction Model for English Learning With Different Learning Motivations

  Open Access•Huan Wang, Xiaoyi Ge et al.•SAGE Open•2026

  Add to list

• Machine learning

  Open Access•M I Jordan, T M Mitchell•Science•2015

  Add to list