#Bank loan risk engine verification

Data analysis technique is the multiple linear regression, hypothesis testing while using T - test to examine the effect of partial variables and F - test to examine the effect of variables simultaneously with a significance level of 5 %.

#Bank loan risk engine verification

The method used is descriptive and verification method, with secondary data from financial statements of 26 Regional Development Bank in Indonesia as a research object units. Credit risk as measured by non performing loans (NPLs), operational efficiency as measured by ratio of operating expense to operating income (OEOI) and banking profitability as measured by return on assets (ROA). The aim of this study was to analyze the effect of credit risk and operational efficiency to the banking profitability. The study recommended that commercial banks should effectively respond to CBK interest rate policies, minimize number of bad loans and strive to maintain low PAR. Findings revealed that Central bank (CBK) policies affect the interest rates charged (mean 4.23), interest rates charged vary depending on repayment period (mean 3.85), higher portfolio at risk (PAR) increases the number of NPLs (mean 3.77) and increased interest income promotes high performance by banks (mean 3.94).There was a statistically significant relationship between interest rates, loan provision and performance of commercial banks. The collected data was collated and coded for descriptive and inferential analyses using the Statistical Package for Social Sciences version 23. Stratified random sampling was used to select a sample of 111 respondents who were administered with a structured questionnaire. Descriptive research design was adopted with a target population of 153 respondents from the credit departments. This study focused on interest rates and their effect on performance of the commercial banks in Kenya. The success of a commercial bank depends on income and value of its assets (loans). The overview findings showed that there is significant relationship between non-performing loans and bank’s profitability and there is a negative significant relationship between loans and advances and banks performance and there is no significant relationship between amount of credit and banks profitability. Amount of credit was measured by loan and advances to customers divided by total assets, nonperforming loans was measured using nonperforming loans/ total loans, and profits were measured using ROTA (Return on Total assets), as profits for the year to total assets. Nonperforming loans was expressed as a proportion of the total loans extended by the commercial banks.

Amount of credit was expressed as a proportion of total assets to control for the size of the banks. The researcher chose NBM, NBS, FDH and ECO Bank as case study of the research, where Amount of credit was measured by loan and advances extended to customers and nonperforming loans were used as proxies for credit risk. In order to answer the research objectives, data was collected through questionnaires that were administered to various respondents in banks and annual reports that were analysed through a regression model which was used to establish the relationship between amount of credit, non-performing loans, loans and advances and profits during the period of study. The research was an attempt to assess the extent to which credit risk management impacts on the financial performance in commercial banks of Malawi. The main goal of this study was to analyse the credit risk management efficiency in Malawi’s commercial banking sector on financial performance for the period 2011-2014.