In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.

In the present economic scenario, money plays a vital role as edge money is essential to meet out the crisis in the future. The main focus of this study is to evaluate the role of Artificial Intelligence (AI) in the transformation of the global services industry known as Financial Technology (FinTech) in a faster pace. A highly competent and accessible FinTech services offered by AI, the challenges it encounters during the transformation, and guidance for policies of AI and FinTech services are the areas that are being addressed in this research paper. The aim of saving money is to invest it in some FinTech products and services to get more returns in a long span. However, there is more chaos among individuals in gaining knowledge. It is also time-consuming because of the investors' inability to process the available information-one of the issues that demand investigation using the AI techniques in Stock Market (SM) prediction. Predicting and modelling the future price of a Stock Market Index (SMI) by applying AI techniques on the basis of its historical price information is the prime goal of this research study. Besides, possessing a comprehensive idea of the existing investment options is also essential to making good financial investment decisions. The results show that various B Ravi Kumar Bommisetti