• Machine learning to avoid any inaccuracy caused by manual staging and manual segmentation
      • Achieve economic benefits while introducing regulatory requirements
      • Credit spread adjusted pricing
      • Early warning to trigger forbearance and modification

Regulatory obligations impact business in various ways. For compliance purposes, business processes need to be adjusted and new control mechanisms 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 

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

The expected credit loss (ECL) for an existing transaction is calculated during regulatory analysis and posted to risk provisioning with an immediate effect on income. When ECL expectations later become reality, the provision for credit risks is written back at the expense of a write-down without any impact on the P&L at this point in time.

On the other hand, an attempt is made from an economic point of view, to limit the "feared" credit loss operationally and to start with the loan application or to integrate it into credit management as an early warning system. From a business perspective, it is all about credit risk and how to consider it in:

The regulatory ECL, as well as parameters used while calculating ECLs, can be reused in an economic business environment.

But with regard to ...

 Figure: Segmentation in the conventional approach and machine learning


Conventional approaches are sufficient and accepted for regulatory requirements. Of course, they can also be generally used for business analysis.
However, in order to improve business decisions, an approach should be applied that considers the full individual customer DNA and the entirety of the deal parameters. Machine learning considers all this during the training of the neuronal network. Any inaccurancy caused by manual staging and manual segmentation can be avoided.


Figure: Making use of the entire customer DNA in a neuronal network