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It does not need to be mentioned that Business cards are essential for businesses and consumers alike across
all industries irrespective of size of the business. Today, these cards not only help in giving contact details but
building a brand. In digital era the business card is also going through the journey of digital transformation.
While some of...
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... for specific business processes in a business process diagram; it's also represents the standard graphical interfaces of interactive process between HumanComputer [6]. We have used standard the BPMN to implement the Business process of cloud EBC system. The human-computer interface interactive diagram of cloud EBC system is shown as below Fig. ...
Citations
... It has long attracted the attention of scientists. Numerous approaches to the problem have been studied, including credit scoring and evaluation, credit rating, bankruptcy prediction (Hosaka, 2019), loan/insurance underwriting (Chi et al., 2021), bond rating, loan application, consumer credit determination, corporate credit rating, mortgage selection decision, financial distress prediction, and business failure prediction (Chen et al.,2020). Determining the risk status of an asset is crucial to its valuation. ...
As a result of a convergence of trends, the pandemic is already regarded
as a pivotal moment in the evolution of banking. In order to adapt and innovate,
banking institutions are more proactive as a result of COVID-19 and question
long-held assumptions. In today’s society, people are more likely to look
forward than to reflect on their past achievements. In real time, COVID-19 is
a landmark event for the financial services and banking industries. Although
it is difficult to identify a turning point in the present, the evidence in this case
is so compelling that it is difficult to dispute. The purpose of this article is to
examine the major trends in the banking industry and provide a list of those
with the most potential impact.
Keywords: Banking Sector, Trends, Covid-19, Bank risk management
Data availability and accessibility have brought in unseen changes in the finance systems and new theoretical and computational challenges. For example, in contrast to classical stochastic control theory and other analytical approaches for solving financial decision-making problems that rely heavily on model assumptions, new developments from reinforcement learning (RL) can make full use of a large amount of financial data with fewer model assumptions and improve decisions in complex economic environments. This paper reviews the developments and use of Deep Learning(DL), RL, and Deep Reinforcement Learning (DRL)methods in information-based decision-making in financial industries. Therefore, it is necessary to understand the variety of learning methods, related terminology, and their applicability in the financial field. First, we introduce Markov decision processes, followed by Various algorithms focusing on value and policy-based methods that do not require any model assumptions. Next, connections are made with neural networks to extend the framework to encompass deep RL algorithms. Finally, the paper concludes by discussing the application of these RL and DRL algorithms in various decision-making problems in finance, including optimal execution, portfolio optimization, option pricing, hedging, and market-making. The survey results indicate that RL and DRL can provide better performance and higher efficiency than traditional algorithms while facing real economic problems in risk parameters and ever-increasing uncertainties. Moreover, it offers academics and practitioners insight and direction on the state-of-the-art application of deep learning models in finance.
