Credit Risk Management for Mongolian Banks Essay

The immenseness of optimal decision-making and precise predictions is not limited to banks only but likewise of importance to other financial institutions. Nowadays, financial markets be becoming progressively uncertain and interdependent, making accurate prediction of future market directions a near impossible task. Although, in case of Mongolia, some econometric models are being tested for the last two decades, practical application is lustreless and it is common practice for businessmen to make decisions based on suspiciousness and gut feeling. Unfortunately, this unscientific approach to decision making is quite commonplace. The design of this research is to overcome conditions and to identify the best evaluation model for credence peril forecasting for banking institutions. From a theoretical point of view, this research newsprint introduces a literature review on the application of back university extension algorithm of an artificial neural intercommunicate, linear p robability model, and double star survival (logit probit) model for credit risk oversight.Whereas, from an empirical point of view, this research compares the econometric models and artificial neural network using Mongolian banks credit risk data, and shows the differences between the aforementioned four models. We demonstrate that artificial neural network model is more convenient for Mongolian banks credit risk management than other econometric models due to the models evaluation and forecast accuracy. Therefore, we recommend Mongolian banks and financial institutions to apply ANN model to forecast credit risk and to turn off risk. Key words credit risk management, linear probability model, binary choice logit and probit model, artificial neural network, back propagation algorithm, forecasting.