Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model

Abstract

Recently, Financial Technology (FinTech) has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm. Financial crisis prediction (FCP) is an essential topic in business sector that finds it useful to identify the financial condition of a financial institution. At the same time, the development of the internet of things (IoT) has altered the mode of human interaction with the physical world. The IoT can be combined with the FCP model to examine the financial data from the users and perform decision making process. This paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder (MOSSA-SAE) model for FCP in IoT environment. The MOSSA-SAE model encompasses different subprocesses namely preprocessing, class imbalance handling, parameter tuning, and classification. Primarily, the MOSSA-SAE model allows the IoT devices such as smartphones, laptops, etc., to collect the financial details of the users which are then transmitted to the cloud for further analysis. In addition, SMOTE technique is employed to handle class imbalance problems. The goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of SMOTE. Besides, SAE model is utilized as a classification technique to determine the class label of the financial data. At the same time, the MOSSA is applied to appropriately select the 'weights' and 'bias' values of the SAE. An extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct aspects. The experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.