Predicting Loan Defaulters by using Bayesian Network in Soft Computing

Soft Computing is a technique that includes artificial neural networks, genetic algorithms, fuzzy logic, Bayesian networks and Probabilistic Techniques etc. It differs from the traditional computing and admits approximate reasoning, imprecision, uncertainty and partial truth in order to imitate the remarkable human capability of making decisions in real-life. Soft Computing has therefore become popular in developing systems that epitomize human expertise. Bayesian network has so many applications in real world. For instance Medical Diagnosis & Clinical Decision Support, Complex Genetic Models, Crime and terrorism management domain: e.g. Risk factors analysis, Inference in Forensic Science, Terrorism Risk Management. Financial and business domain: e.g. Credit-Rating of Companies, Predicting Probability of Default for Large Corporate, Manufacture monitor and control domain: e.g. Reliability Analysis of Systems with Dynamic Dependencies, Decision Support on Complex industrial Process Operation etc. In this paper I have presented about how Bayesian network will help us to find loan defaulters using soft computing.