استخدام الشبكة العصبية الاصطناعية في تحليل أثر إدارة المخاطر المصرفية على ربحية البنوك الإسلامية اليمنية

Abstract EN

The Study aimed at identifying the impact of banking risk management on Yemeni Islamic banks' profitability.

To achieve this، the current study used descriptive analytical methods، statistical methods and sensitivity analysis using artificial neural network in analyzing the data collected on the banking risk indicators and profitability indicators during the period (2006-2015)، from a sample consisting of three Islamic banks (Tadhamon Islamic Bank، Saba Islamic Bank، Shamil Bank of Yemen & Bahrain).

the important main results of the study reached: The positive impact of banking risk management on the profitability of Islamic banks in Yemen.

As the direction of Islamic banks in Yemen to raise the indicators of banking risks، would move the indicators of profitability to the top.

It can also manage profitability by controlling the risks using standard or substandard mathematical models ، and that the best model is to achieve the exact match of the quality indicators of the model، and that the entrance sensitivity analysis used in the study according to the models of the neural network is appropriate to explain the sensitivity of profits to the relative change in risk management.

The study recommends using the proposed mathematical models to manage profitability through risk control.

The study also recommends that Islamic banks stop excessive changes in credit risk rates by reducing doubtful debts، as their rise negatively affects Islamic banking profitability indices.