Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 28, Pages: 1-6
Amandeep Kaur, Vishu Madaan, Prateek Agrawal* , Ranjit Kaur and Sanjay Kumar Singh
School of Computer Science Engineering, [email protected]
*Author for correspondence
School of Computer Science Engineering
Background/Objectives: Banking sector faced many problems like credit assessment, credit worthiness, and credit risk etc. Many techniques are used to solve these problems in artificial intelligence. But these problems are not fully resolved in previous years. Methods/Statistical Analysis: One of the most important problems is credit risk as customer defaulter risk. Recent studies have not discussed about defaulter or non-defaulter customer. This research work has discussed about customer’s defaulter risk as well as credit risk. Findings: A fuzzy expert system has been designed which can categorized customer as defaulter and non- defaulter. The defaulter risk is calculated by considering factors as CS (CIBIL Score), LVR (Loan to Value Ratio), AAL (Already Availed Loan), IRF (Income Ratio Factor). Data of customers are collected from Indian Overseas Bank (IOB) branches. Different defuzzification methods are used to verify the result of customers. It also verified by the expert in banking domain. Further it is also tested on the data of other branches of banks. Application/ Improvements: The system can be helpful for the bank employees in decision making process. In addition to this the system can be used for training new employee for loan approval tasks.
Keywords: Banking Sector, Credit Risk, Defaulter, Fuzzy Expert System, Intelligent System
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