This calculates risk provisions for portfolios containing assets with similar risk attributes (stages 1 and 2). The ready-to-go solution contains an easy-to-use UI that a business department can use without IT support.
A workbench, which assists the capture, simulation, monitoring and correction of the expected cash flows from non-performing financial assets, is available for the individual risk provisions needed for stage 3. Intra and inter-departmental processes are digitalised by the Impairment Workbench and thus made more transparent and traceable.
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 app identifies criteria that point to an adverse business situation.
IFRS 9 calls for the segmentation of financial assets on the basis of similar credit risk characteristics. The expected credit loss needs to be calculated for each segment, taking probability-weighted macroeconomic scenarios into account.
In contrast to the conventional segmentation/portfolio formation of loans, machine learning compares each loan with another and calculates it on the basis of ECL.