DATA SCIENCE IN FINANCIAL SERVICES: RISK ANALYSIS, FRAUD DETECTION, AND MARKET INSIGHTS

Abstract

One area of interdisciplinary academic study that is thought to offer deep business insights is data science. The study's objective is to evaluate the value of market, risk, and fraud analysis in data science for enhancing financial services.

Literature review: It is well recognised that using data science to conduct a risk analysis influences commercial organisations' decision-making when it comes to starting new projects. To enhance the banking sector's services, data science's fraud detection division is required. A more complex sort of data science called market analysis can give businesses important insights.

Methodology: A study's methodological choice is crucial since it determines the validity and dependability of the research endeavour. In this instance, it may be said that the study adhered to a theoretical analysis as including theories aids in the investigation of the research problem.

Findings and analysis: In the context of data science, risk prediction models are helpful since they facilitate the start of statistical analysis. In this instance, it should be noted that quick fraud detection is made possible by the efficiency of data processing and encoding. Thus, putting in place such a network can enhance market data analysis by helping to unravel data layers and patterns.

Conclusion: The purpose of the study is to critically examine the effects of fraud detection, risk analysis, and market analysis. This project has focused on leveraging data science to improve financial services. It has been demonstrated that the study's critical evaluation has a significant influence on how data science is applied in the financial industry