Customer churn prediction (CCP) is among the greatest challenges faced in the telecommunication sector. With progress in the fields of machine learning (ML) and artificial intelligence (AI), the possibility of CCP has dramatically increased. Therefore, this study presents an artificial intelligence with Jaya optimization algorithm based churn prediction for data exploration (AIJOA-CPDE) technique for human-computer interaction (HCI) application. The major aim of the AIJOA-CPDE technique is the determination of churned and non-churned customers. In the AIJOA-CPDE technique, an initial stage of feature selection using the JOA named the JOA-FS technique is presented to choose feature subsets. For churn prediction, the AIJOA-CPDE technique employs a bidirectional long short-term memory (BDLSTM) model. Lastly, the chicken swarm optimization (CSO) algorithm is enforced as a hyperparameter optimizer of the BDLSTM model. A detailed experimental validation of the AIJOA-CPDE technique ensured its superior performance over other existing approaches.

Accurately predicting and anticipating financial crises becomes of paramount importance in the rapidly evolving landscape of financial technology (Fintech). There is an increasing reliance on predictive modeling and advanced analytics techniques to predict possible crises and alleviate the effects of Fintech innovations reshaping traditional financial paradigms. Financial experts and academics are focusing more on financial risk prevention and control tools based on state-of-the-art technology such as machine learning (ML), big data, and neural networks (NN). Researchers aim to prioritize and identify the most informative variables for accurate prediction models by leveraging the abilities of deep learning and feature selection (FS) techniques. This combination of techniques allows the extraction of relationships and nuanced patterns from complex financial datasets, empowering predictive models to discern subtle signals indicative of potential crises. This study developed an extended osprey optimization algorithm with a Bayesian NN to predict financial crisis (EOOABNN-PFC) technique. The EOOABNN-PFC technique uses metaheuristics and the Bayesian model to predict the presence of a financial crisis. In preprocessing, the EOOABNN-PFC technique uses a min-max scalar to scale the input data into a valid format. Besides, the EOOABNN-PFC technique applies the EOOA-based feature subset selection approach to elect the optimal feature subset, and the prediction of the financial crisis is performed using the BNN classifier. Lastly, the optimal parameter selection of the BNN model is carried out using a multi-verse optimizer (MVO). The simulation process identified that the EOOABNN-PFC technique reaches superior accuracy outcomes of 95.00% and 95.87% compared with other existing approaches under the German Credit and Australian Credit datasets.