Artificial intelligence based optimal functional link neural network for financial data Science

Abstract

In present digital era, data science techniques exploit artificial intelligence (AI) techniques who start and run small and medium-sized enterprises (SMEs) to have an impact and develop their businesses. Data science integrates the conventions of econometrics with the technological elements of data science. It make use of machine learning (ML), predictive and prescriptive analytics to effectively understand financial data and solve related problems. Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations. At the same time, it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis. AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems. With this motivation, this paper presents a new AI based optimal functional link neural network (FLNN) based financial crisis prediction (FCP) model for SMEs. The proposed model involves preprocessing, feature selection, classification, and parameter tuning. At the initial stage, the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data. Besides, a novel chaotic grasshopper optimization algorithm (CGOA) based feature selection technique is applied for the optimal selection of features. Moreover, functional link neural network (FLNN) model is employed for the classification of the feature reduced data. Finally, the efficiency of the FLNN model can be improvised by the use of cat swarm optimizer (CSO) algorithm. A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model. The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%, 92.100%, and 95.220% on the applied Polish dataset Year I-III respectively.