Indian Journal of Recent Development Systems for Digitization

ISSN: 2584-1009 (Online)

Risk Management Using Data Science Approaches

Author(s)

Sivakumar, R.D. * and Brindha, S.**

*  Assistant Professor, Department of Computer Science, Bell Institute, Sivakasi

** Former Assistant Professor of Business Administration, Sivakasi

Abstract

Risk management is a vital component in the decision-making process, for instance, in the financial, healthcare and cybersecurity domains. Nowadays, in the time which is the era of explosive growth of data, where the data science approaches have shown to be powerful tools of risk management management. This study reviews using data science techniques to detect, describe, and minimize risk through various sectors. Data science uses advanced algorithms and statistics models to extract valuable insights from large datasets so that organizations can take informed decisions in a risk management process. The algorithms of machine learning have become important tools in the risk management operations because they use the data of the past in order to recognize the trend patterns and predict the outcomes. The technique also ensures the detection of anomalies such as abnormal behaviors or patterns which are considered risks or fraudulent activities. Data science data models are widely implemented in financial to determine credit risks, portfolio optimization or fraud detection. Historical market data and financial indicators can be analyzed by predictive models in order to determine the likelihood of default or to assess the risk/return tradeoff in investment portfolios. By the same token, in healthcare, the data-driven approaches which have the ability to identify patient risks, bring out the best of the available treatment plans, and predict disease outbreaks, are also being used. Moreover, data science plays essential role in cybersecurity, by identifying and preventing cyber threats during the process. Machine learning algorithms, built on the analysis of network traffic, user behaviors and system logs, can point out suspicious activities as well as potential vulnerabilities, consequently increasing the overall security level of the system. The use of data science within the risk management process has a number of advantages, such as better assessment of risk, faster decision-making, and the possibility to proactively deal with risks. However, data quality issues, the explainability of the models, as well as moral concerns are among the safety factors to enable reliable and efficient risk management of the data-based solutions. Eventually the data science techniques utilized in risk management yields the organizations a better grasp of classifying, evaluating, and overcoming risks in different sectors. Utilizing innovative analytical methodologies and smart data applications, organizations will be able to strengthen their ability to react to some uncertainties that they may face and make better decisions at a business atmosphere that is complex and dynamic.

Keywords: Risk Management, Data Science, Machine Learning, Predictive Modeling, Cybersecurity, Decision-making, Fraud Detection, Credit Risk Assessment, Portfolio Optimization, Real-time Detection and Data-driven Approaches.

Volume : 1

Issue : 5

Pages : 39-46

Date of Publication : April 2024

DOI : 10.5281/zenodo.10925881

Published By

IJRDSD

Published In

Indian Journal of Recent Development Systems for Digitization