Abstract:
The research aims to find new approaches in bank sustainability assessment. During the
last 15 years banks in Kazakhstan were affected by several global crises. Those events led
to significant problems within the financial sector, which resulted in serious consequences.
Untimely and incomplete identification of problems required governmental financial
support or even liquidation of those banks. One basic problem appears to be the lack of an
appropriate mechanism for preventive reaction. This thesis uses an approach of estimating
bank’s sustainability via invented Bank Sustainability Index (BSI) and Weighted Average
Bank Sustainability Index (WABSI). BSI reflects liquidity, capital adequacy and credit
portfolio nature of the bank. Such indicators help to determine the current position of a
particular bank as well as the overall performance of the bank sector at a given point of
time. Machine learning techniques were used as practical tool for models development.
Those models allow one to make predictions and reveal which economic indicators most
affect the financial system. The research provides comprehensive overview of the banking
sector of Kazakhstan and analysis of reasons of default cases.
