Credit Risk of Bank Customers: Proposed Models for Predicting and Assessing Credit Risk for Classifying Bank Customers Based on Machine Learning and Evolutionary Algorithms

Traditionally, static models are used to model credit bank patterns, but economic factors are independent of political fluctuations; as the political atmosphere changes, the economic environment also changes with it. This is especially evident in Iran after the 2008-2016 US sanctions, as it is highly likely that they were unable to repay their debts (i.e. became bad customers). It is necessary to create dynamic models that incorporate various political-economic factors, which can be combined to propose hybrid models for credit assessment relative to bank classification based on classification algorithms and proposed algorithms. The model can have two stages, the first stage of which is data preprocessing (to eliminate data defects) resulting from the feature selection function, that is, selecting the appropriate features from among the existing features in the feature set. The second stage of the proposed system is the result of the work and the main output of the proposal, namely the classification of customers, who are divided into two categories, namely those who are eligible to borrow and those who have the conditions to qualify for borrowing, where appropriate classification techniques can be used in this part. In fact, the main objective of this review is to propose models for credit prediction and assessment in terms of bank classification based on machine learning and developmental algorithms.