Bank loan analysis using customer usage data: A big data approach using Hadoop

As of now, currently there is a tremendous rise in the economy development due to which there has been a huge rise in the requirement of the personal loan of customers as the behavior of the borrowers have uncertainty and fuzzy nature. For both lenders and borrowers, credit risk is a major challenge, which directly or indirectly affects the reliability of the banks. Present article has concentrated on menace by granting loans to the customers, risk related to the investors. The objective of this paper is to analyze the credit risk and loan performance of the “Lending Club” company which is one of the biggest market place for online credit. Analyses of the performance of the bank loan and credit risk on the large dataset having 112 attributes which have been collected from the Lending Club of the period 2012 and 2016. In this paper, Hadoop approach has been used and for applying Hadoop methodology we will be using the Cloudera software which is an open source platform for analyzing the data. It supports the Hadoop ecosystem which is used for the managing, storing and analyzing the large volume of data. In this article, we used the Hive which is data warehouse system and which is used for managing and analyzing the data stored in HDFS (Hadoop Distributed File System) using HiveQL. To understand the performance of the bank loan data we had performed various analyses on the collected dataset of the bank.