Framework to predict NPA/Willful defaults in corporate loans: A big data approach

Research output: Contribution to journalArticlepeer-review

Abstract

Growth and development of the economy is dependent on the banking system. Bad loans which are Non-Performing Assets (NPA) are the measure for assessing the financial health of the bank. It is very important to control NPA as it affects the profitability, and deteriorates the quality of assets of the bank. It is observed that there is a significant rise in the number of willful defaulters. Hence systematic identification, awareness and assessment of parameters is essential for early prediction of willful default behavior. The main objective of the paper is to identify exhaustive list of parameters essential for predicting whether the loan will become NPA and thereby willful default. This process includes understanding of existing system to check NPAs and identifying the critical parameters. Also propose a framework for NPA/Willful default identification. The framework classifies the data comprising of structured and unstructured parameters as NPA/Willful default or not. In order to select the best classification model in the framework an experimentation is conducted on loan dataset on big data platform. Since the loan data is structured, unstructured component is incorporated by generating synthetic data. The results indicate that neural network model gives best accuracy and hence considered in the framework.

Original languageEnglish
Pages (from-to)3786-3797
Number of pages12
JournalInternational Journal of Electrical and Computer Engineering
Volume9
Issue number5
DOIs
Publication statusPublished - 01-10-2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Framework to predict NPA/Willful defaults in corporate loans: A big data approach'. Together they form a unique fingerprint.

Cite this