The credit spread is required for the credit application process. The credit spread should take into account the expected credit loss for an individual deal. As an alternative to conventional methods of scoring, rating and PD determination, Machine Learning can also be used, for this purpose, on the basis of a performance database filled with Deep Learning.
FlexFinance provides an API that can be integrated into the credit application process. We have other solutions for credit monitoring, accounting and backtesting.
The most significant impact on overall bank management is caused by the introduction of the Expected Credit Loss approach to reflect credit risk in external accounting.
IFRS 9 calls for the segmentation of financial assets on the basis of similar credit risk characteristics. For each segment, the expected credit loss needs to be calculated taking probability-weighted macroeconomic scenarios into account.
In contrast to the conventional segmentation/portfolio formation of loans, FlexFinance offers the ECL calculation on the basis of machine learning.
The application monitors the loans for which contracts already exist. Not only are customers and contract data taken into account, but macro- and microeconomic factors that naturally influence credit management are also considered. Based on deep learning processes and machine learning, the EWS application identifies criteria that point to an adverse business situation.
The EWS application initiates a workflow when certain events occur. The events could also be the variance of the ECL, for example. The workflow actions could also be linked to contract deadlines in such a way that realistic options for action exist.