Credit Risk Analysis Using Machine Learning Models

  Sandip Chobhe,  Shubham Madalapure,  Sanket Chandake,  Akshay Baraskar

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. credit risk evaluation is one of the critical and biggest challenge faced by banks accuracy plays very important role in classification of credit data to avoid financial loss. in Banking industry has the major activity of lending money to those who are in need of money. In order to payback the principle borrowed from the depositor bank collects the interest made by the principle borrowers. Credit risk analysis is becoming an important field in financial risk management Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability In the given paper we are study different techniques for the credit risk analysis which are used for the evaluation for the credit risk data sets.

Keywords- Machine Learning, Credit risk; Machine learning; Bayesian classifier; Naive Bayes classifier; Decision tree; KNN; K-means clustering.

Unique Identification Number - IJEDR1904052
Page Number(s) - 296-298
Pubished in - Volume 7 | Issue 4 | October 2019
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Sandip Chobhe,  Shubham Madalapure,  Sanket Chandake,  Akshay Baraskar,   "Credit Risk Analysis Using Machine Learning Models", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.7, Issue 4, pp.296-298, October 2019, Available at :http://www.ijedr.org/papers/IJEDR1904052.pdf