A good practice in credit risk management is to turn periodic core banking data into meaningful information for ongoing portfolio monitoring and loan loss analysis. A process of getting a raw data transformed into meaningful information to support business decisions can be termed as business intelligence or simply (BI). This could help banks and other commercial lenders with a better visibility to manage commercial risks across commercial credit portfolios.
Commercial credit risk arises when borrowers cannot fulfill obligations to make loan repayments. It is usually calculated with the formula
Expected Losses = EAD X PD X LGD
Exposure at Default (EAD) is the expected value of the loan at the time of default;
Probability of Default (PD) measures the likelihood that the borrower default;
Loss Given Default (LGD) measures the amount of loss if there is a default, expressed as a percentage of EAD.
The primary purpose in conducting loss analysis is to examine how the loan losses affect the profit as the banks and financial institutions are expected to hold reserves against expected losses, which is considered the cost of doing business.
Estimating the main risk factors accurately provides credit risk managers with a foundation for timely and informed decision-making and pricing commercials loans appropriately in accordance to an organization’s risk appetite.
Dashboard and reports can be built around several key risk dimensions, including Non-performing loan analysis, portfolio credit exposure and facilities security analysis. Users can view, for example, delinquencies as a percentage of the total lending portfolio by borrower segment, by product type, by geography.
A combination of raw risk data into risk intelligence can now allow the user to make informed business decision around credit risk.
In addition to the above mentioned reporting system, moving from raw risk data into BI can be very useful for banks and financial institutions as it can help you forecast borrower behavior trend. Also based on historical data, organizations can define the characteristics of the SME who are unlikely to repay the loan back and thereby reduce credit risk.
Business Intelligence can assist you in better credit risk decisions.