| Year |
2024 |
| Volume/Issue/Review Month |
Volume 1 | Special Issue | October |
| Title |
Predictive Analytics in Enterprise Risk Assessment: A Machine Learning Perspective |
| Authors |
Narayana Maharana, Suman Kalyan Chaudhury , Chandra Sekhar Patnaik, Sudesh Kumar Kuppili |
| Broad area |
Predictive Analytics in Enterprise Risk Assessment: A Machine Learning Perspective |
| Abstract |
Scientific risk assessment serves as a crucial assurance for the sustainable growth of
businesses. With the continual progress and maturation of machine learning technology, its
significance in the realm of data prediction and risk assessment has become pivotal. This study
investigates the utilization of machine learning in assessing enterprise risks, employing three
distinct algorithms—namely, support vector machine (SVM), random forest (RF),and AdaBoost.
The initial step involves establishing comprehensive risk assessment indexes for enterprises,
capturing diverse risks through various parameters. Then, utilising previously collected
secondary data, the three machine learning algorithms were trained to develop a reliable risk
evaluation model. Lastly, the risk indices were produced by the applied risk assessment model
using a collection of current risk indicators. The experimental phase involves the analysis and
validation of the method using real data, demonstrating the efficacy of the proposed machine
learning algorithms in accurately evaluating enterprise risks. |
| File |
 |
| Referenceses |
|