Nurul Alam
Papers
1
Total Citations
30
H-Index
1
About
Nurul Alam is a leading researcher in financial risk modeling and corporate failure prediction, with a focus on integrating advanced machine learning techniques into traditional econometric frameworks. His most cited work, "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models" (2021), has garnered 30 citations and stands as a pivotal contribution to the field. In this study, Alam systematically compares deep learning architectures—such as neural networks—against discrete hazard models, demonstrating that deep learning approaches can significantly enhance predictive accuracy for corporate bankruptcies, especially in handling complex, non-linear relationships in financial data. This work bridges the gap between cutting-edge AI and practical risk assessment, offering actionable insights for financial institutions and regulators. Beyond this, Alam’s research has influenced the development of more robust early-warning systems for financial distress, contributing to safer lending practices and improved corporate governance. His interdisciplinary approach, combining econometrics, computer science, and finance, makes his findings highly relevant for students and researchers exploring the future of predictive analytics in economics. Alam’s contributions continue to shape how scholars and practitioners leverage data-driven methods to anticipate and mitigate corporate failures.
Research Focus
Key Achievements
Top Papers
- 1