model ifrs 9 expected credit loss with matlab
ifrs 9 is the international financial reporting standard that addresses the accounting of financial instruments, such as loans, mortgages, and other credit instruments. the directive has three core components: classification of instruments, impairment calculation, and hedge accounting.
because credit instruments are at risk of default, accounting for instruments in accounts must consider likelihood of future impairment, through expected loss and lifetime expected credit loss. model-oriented approaches are required. credit and regulatory risk teams thus perform ifrs 9 tasks, such as:
- asset classification, through statistical and machine learning methods
- macroeconomic modeling
- scenario generation and scenario analysis
- stochastic modelling of default and recovery
- automated reporting, reflecting point-in-time model and data selection, which may incur regulator scrutiny
popular tools include matlab®, statistics and machine learning toolbox™, econometrics toolbox™, risk management toolbox™, and matlab report generator™.
examples and how to
software reference
see also: econometrics and economics, monte carlo simulation, credit scoring model, risk management solutions, cecl with matlab, expected credit loss, basel iv, fraud analytics, modelscape,