Finance

Actuarial science

Debt

Political science

Credit risk

Econometrics

Economics

Microeconomics

Judgement

Capital requirement

Incentive

Computer science

Basel II

Financial Innovation

Giving loans and issuing credit cards are two of the main concerns of banks in that they include the risks of non-payment. According to the Basel 2 guidelines, banks need to develop their own credit risk assessment systems. Some banks have such systems; nevertheless they have lost a large amount of money simply because the models they used failed to accurately predict customers’ defaults. Traditionally, banks have used static models with demographic or static factors to model credit risk patterns. However, economic factors are not independent of political fluctuations, and as the political environment changes, the economic environment evolves with it. This has been especially evident in Iran after the 2008–2016 USA sanctions, as many previously reliable customers became unable to repay their debt (i.e., became bad customers). Nevertheless, a dynamic model that can accommodate fluctuating politico-economic factors has never been developed. In this paper, we propose a model that can accommodate factors associated with politico-economic crises. Human judgement is removed from the customer evaluation process. We used a fuzzy inference system to create a rule base using a set of uncertainty predictors. First, we train an adaptive network-based fuzzy inference system (ANFIS) using monthly data from a customer profile dataset. Then, using the newly defined factors and their underlying rules, a second round of assessment begins in a fuzzy inference system. Thus, we present a model that is both more flexible to politico-economic factors and can yield results that are max compatible with real-life situations. Comparison between the prediction made by proposed model and a real non-performing loan indicates little difference between them. Credit risk specialists also approve the results. The major innovation of this research is producing a table of bad customers on a monthly basis and creating a dynamic model based on the table. The latest created model is used for assessing customers henceforth, so the whole process of customer assessment need not be repeated. We assert that this model is a good substitute for the static models currently in use as it can outperform traditional models, especially in the face of economic crisis.

A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks