Non-performing Asset Analysis Using Machine Learning

Abstract

Loans are given by the banks to the customer, according to their needs, and are being monitored by various entities within the organization, so that adequate amount is received to the customers and assuring that they would not default or certain financial advisories can help them in letting know whether a non-performing assets (NPA) case can be triggered or not. Though there are many softwares in market which can do this job quite well, but for small financial institutions and banks, it becomes a big hurdle to deploy such solution. Unfortunately, there has not been a cost-effective method of identifying and predicting these cases and has also led to some failure in monitoring of NPA activities, in organizations. Therefore, an effective approach is to use a machine learning algorithm, with the use of open-source softwares, that could help in predicting the NPAs by using the essential parameters and thereby giving a cost-effective solution to such an issue, which many of the banking and financial organizations are facing.