An Intelligent Internet-of-Things-Aided Financial Crisis Prediction Model in FinTech

Financial technology (FinTech) has become a hot research topic recently, moreover, an area of major investment for most financial institutions. As the FinTech unleashed many new strategic solutions for major financial problems. One of those pivotal strategies of FinTech is financial crisis prediction (FCP) that dictates the financial status of an institution. Also, the rise of the Internet-of-Things (IoT) technology has paved a new way for interaction between humans and the physical world. Therefore, IoT can feasibly be incorporated into the FCP model to obtain a real-time analysis of the financial data from the clients. With this perspective, we propose an intelligent IoT-aided FCP model using metaheuristic algorithms. The proposed FCP method comprises data acquisition, preprocessing, feature selection (FS), and classification. First, the financial data of the enterprises are collected using IoT devices, such as smartphones, laptops, etc. Next, the quantum artificial butterfly optimization (QABO) approach for FS is applied to choose an optimal set of features. Afterward, long short-time memory (LSTM) with recurrent neural network (RNN) model is employed to classify the collected financial data. An exhaustive experimental validation process is carried out to ensure the performance of the proposed QABO-LSTM-RNN model. The simulation results accredited the efficacy of the proposed model contrasted with other baseline methods in terms of best cost, sensitivity, specificity, accuracy, F-score, kappa, and Mathew correlation coefficient (MCC).