ABSTRACT
Global crises such as the COVID-19 pandemic and other recent environmental, financial, and economic disasters have weakened economies around the world and marginalized efforts to build a sustainable economy and society. Financial crisis prediction (FCP) has a significant impact on the economy. The growth and strength of a country's economy can be gauged by accurately predicting how many companies will fail and how many will succeed. Traditionally, there have been a number of approaches to achieving a successful FCP. Despite this, there is a problem with the accuracy of classification and prediction and with the legality of the data that is being used. Earlier studies have focused on statistical, machine learning (ML), and deep learning (DL) models to predict the financial status of a company. One of the biggest limitations of most machine learning models is model training with hyper-parameter fine-tuning. With this motivation, this paper presents an outlier detection model for FCP using a political optimizer-based deep neural network (OD-PODNN). The OD-PODNN aims to determine the financial status of a firm or company by involving several processes, namely preprocessing, outlier detection, classification, and hyperparameter optimization. The OD-PODNN makes use of the isolation forest (iForest) based outlier detection approach. Moreover, the PODNN-based classification model is derived, and the DNN hyperparameters are fine-tuned to boost the overall classification accuracy. To evaluate the OD-PODNN model, three different datasets are used, and the outcomes are inspected under varying performance measures. The results confirmed the superiority of the proposed OD-PODNN methodology over recent approaches.