TY  - JOUR
AU  - T Sai Lalith Prasad
AU  - K Neeraja Reddy
AU  - G Eeshita
AU  - K Sai Shivani
PY  - 2025
DA  - 2025/06/27
TI  - Improving Bankruptcy Prediction Using Machine Learning
JO  - Global Journal of Engineering Innovations and Interdisciplinary Research
VL  - 5
IS  - 4
AB  - It is very crucial to predict bankruptcy for the decision-making process of creditors, investors,
businesses, and policymakers. While it will help businesses and financial institutions in making sound
judgments by accurately projecting bankruptcy, it helps to reduce the adverse impacts also for the
economy and society. The methodologies like random forest regression and SMOTE along with other
algorithms were cross validated to enhance accuracy. These prediction models can be developed further
by including both financial and non-financial factors like market conditions and management quality.
Moreover, dramatic efficiency improvements can be achieved through advanced technologies like deep
learning. Technological advancement and data accessibility will increase these tactics such that banks
and businesses can discover bankruptcy risk, make the right decisions, and save losses.
SN  - 3066-1226
UR  - https://dx.doi.org/10.33425/3066-1226.1137
DO  - 10.33425/3066-1226.1137