Highlights:
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Regulatory obligations impact business in various ways. For compliance purposes, business processes need to be adjusted and new control mechanism need to be implemented. While supporting compliance as well as the necessary processes, FlexFinance helps to professionalise business and achieve economic benefits.
Figure: FlexFinance helps to achieve economic benefits by fulfilling regulatory obligations
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For example, IFRS 9 calls for the implementation of complex and challenging new regulations on risk provisioning (impairment). Of course the introduction of the expected credit loss model does not impact the payment behaviour of customers itself. The sum of the net payments for a financial instrument does not depend on the GAAP applied. However, IFRS 9 regulates the profit and loss to be disclosed at a certain point in time during the life cycle of a financial instrument. This time factor impacts profit and loss, risk-weighted assets and standard risk costs. In consequence, this time factor ultimately impacts overall bank management, including capital and risk management, regulatory reporting, management of earnings and financial accounting.
Figure: IFRS 9 impacts core elements of overall bank management
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- Staging,
IFRS9 differentiates between 3 stages depending on the significance of the deterioration in credit quality. Depending on the stage assigned, IFRS9 calls for the consideration of 12-month or lifetime ECLs.
As far as early warning is concerned, the differentiation between the 3 stages does not have a sufficient degree of fine granularity. For example, it makes a difference in the probability of default from the perspective of the individual deal if a retail customer is delayed with a due payment for 5 days, 2 weeks or even 4 weeks. Even the probability of success of forbearance or modification measures depends on the right timing which is not necessarily provided by regulatory approaches. - Segmentation,
Conventional approaches work with a rule-based segmentation, applying manually captured settings to identify similar credit risk characteristics using an excerpt of the customer DNA and some product and deal-specific parameters. The calculation of lifetime expected credit losses using parameters based on portfolio/segment, defined using high level similar credit risk characteristics, is fine. However, e.g. for significant deals, the use of regulatory segments to decide on a credit risk adjusted price is questionable. Due to the rough granularity of segments, an entity might not make a deal because the interest offered was too high or it might conclude a deal where the interest does not cover the credit risk.
Figure: Segmentation in the conventional approach and machine learning
Macroecnomic parameters
The macroeconomic parameter is forecasted separately in the conventional approach. Then the parameter forecasted is used as input for the Roll Rate or Migration Analysis.
In the machine learning approach, the forecast of the macroeconomic parameter is integrated into the machine learning process of the neuronal network.
Conventional approaches are sufficient and accepted for regulatory requirements. Of course, they can be generally used for business analysis too. |
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