Research Paper Related To Machine Learning In Financial Sector

Project Title and Abstract

Venkateswara Rao Potini (2016948)

University Canada West

RSCH 600: GRADUATE RESEARCH METHODS

Prof. Hamed Taherdoost

22nd July 2021

 

 

 

 

 

 

 

 

 

 

 

 

 

 

PROJECT TITLE

How emerging technologies can benefit the investment planning: Machine Learning case study.

 

ABSTRACT

Machine learning is a branch of AI that uses statistical models to create predictions. In finance, machine learning algorithms are accustomed detect fraud, automate trading activities, and supply financial advisory services to investors.

There exist numerous applications being employed by huge organizations that are built using machine learning, and that they provide automated financial advice to investors. The applications use algorithms to determine a financial portfolio consistent with an investor’s goals and their risk tolerance.

The cost of those AI implementation is sometimes cheaper than human portfolio managers. While using AI, investors are required to enter their investment or savings goal into the system, and the system will automatically determine the most effective investment opportunities with the greater returns.

For example, an investor who is 30 years old with a savings goal of $500,000 by the time they retire can enter these goals into the application. the application then spreads the investments across different financial instruments and asset classes – like stocks, bonds, assets, etc. – to attain the investor’s long-term goals. the application optimizes the investor’s goals in keeping with real-time market trends to seek out the most effective diversification strategy.

This Research also showcases what’s the necessity to think about AI in finance. How AI reduced operational costs by process automation, Increased revenues by enhanced user experience, gives better compliance and reinforced security.

This study contributes to the literature within the following ways. First, to systematically review the best existing AI Algorithms in Financial sectors for SME’s. Second, to summarize multiple AI Algorithms regarding specified Financial sectors of SME’s and identify the optimal AI Algorithm for required application scenarios. The analysis relies on the data processing methods of Algorithms, which including preprocessing, input data, and evaluation rules. Third, to review attempts to match the technological and application levels of AI with the Financial needs.