Effects of the Artificial Intelligence in the Financial Sector of the US Banks

This research proposal seeks to provide a detailed analysis of the effects of the artificial intelligence in the financial sector of the US banks with specific interest on the Bank of America. Business change is the process of developing and implementing new financial instruments in the financial services sector with the aim of enhancing the operation efficiency and creating a competitive edge. It entails the development of new technologies, products, or services (Wall, 2018). The artificial intelligence is thus categorized into products, process, and institutional change.

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The artificial intelligence is critical to the performance of financial institutions in the banking sectors. It enhances the economic performance by boosting the profitability of the bank therein. Furthermore, information technologies are an essential phenomenon in the modern technological environment setup in the banking industry. In this case, creation and development of new product and services create various changes in the banking sector including stiff competition, deregulation, and the increase in the cost of capital (Nakano, Takahashi & Takahashi, 2018). Nonetheless, banks continue to deploy massive investment in artificial intelligence to sustain the increased competition in the market. For instance, the Bank of America invests heavily in developing innovations in the intelligence sector such as mobile banking.

The general objective of the research proposal covers the instances of the effects of the use of the artificial intelligence techniques in the financial sector on the performance of the US banks. The specific aim is to assess the product innovation effects on the profitability of the Bank of America. The research question of the study is to determine whether artificial intelligence has either a positive or negative impact on the profitability of the Bank of America. It is expected that the findings depict a positive relationship between artificial intelligence and the performance of banks in the USA (Lui & Lamb, 2018). It is because, in the current modern market, the change allows the banks to develop new products and services that enhance the quality of the service delivery enabling consumers to utilize the services more frequently. As the transactions increase, the bank realizes more revenues. Besides, each transaction creates a commission for the bank therein.

The study is significant as it contributes to the development of knowledge on the various stakeholders not only in the banking industry. For instance, it allows the reader to comprehend the effects of such transformations in technology in the financial sector in US banks. The information can help the financial institutions to identify the significant changes that aid the development of good customer relationships. Besides, due to the technological dynamism, the consumer banking preferences and behavior are changing. In this case, banks ought to be proactive to meet these preferences (Rahim et al., 2018). Not only do they enhance the consumer relationships with the bank, but also contribute to the general performance of the bank. Furthermore, customer loyalty contributes to the profitability of the bank. The study can also provide literature to other researchers studying a similar research problem.

In the research methodology, the study integrates graphics research design to determine the relationship between the variables present in the research. The independent variable is the artificial intelligence while the dependent variable is profitability. Further, the study will use secondary data will be available in the annual information technology data of the Bank of America within the last ten years acquired from the Compustat (Rezaee, Jozmaleki & Valipour, 2018). The study will use a multiple linear regression model and the Pearson correlation coefficient to find out the link between the profitability of the banks and the technology intelligence parameters employed in the banks. Furthermore, it will incorporate the Analysis of variance (ANOVA) model to tests the significance of findings.

References

Lui, A., & Lamb, G. W. (2018). Artificial intelligence and augmented intelligence collaboration: regaining trust and confidence in the financial sector. Information & Communications Technology Law, 1-17.

Nakano, M., Takahashi, A., & Takahashi, S. (2018). State space approach to adaptive artificial intelligence modeling: Application to the financial portfolio with a fuzzy system.

Rahim, S. R. M., Mohamad, Z. Z., Bakar, J. A., Mohsin, F. H., & Isa, N. M. (2018). Artificial Intelligence, Smart Contract, and Islamic Finance. Asian Social Science, 14(2), 145.

Rezaee, M. J., Jozmaleki, M., & Valipour, M. (2018). Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in the stock exchange. Physical A: Statistical Mechanics and its Applications, 489, 78-93.

Wall, L. D. (2018). Some Financial Regulatory Implications of Artificial Intelligence. Journal of Economics and Business.