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For the loan application process, FlexFinance provides a credit spread (CS) calculated on PD and LGD in a neuronal network. This CS can be used as a component for calculating the interest during the appliction application process.

Besides optimising the fulfilment of requirements implemented by legal authorities regarding financial accounting and capital requirements, the higher accuracy in calculating expected credit losses combined with improved segmentation supports a pro rata allocation of expected default risks in the internal business segment calculation. This offers new options while defining interest conditions: in determining the interest rate conditions in the context of a loan application, the allocation of credit default risks in line with the originator leads to a "fair" interest rate. From the point of view of individual business, the use of machine learning means that, in the case of higher credit default risks, the associated costs can be identified precisely for the first time when applying for a loan and can be claimed as a contribution to the interest rate conditions. This tends to result in higher net interest income for transactions with greater default risks. This improvement in net interest income is offset by higher loan loss provisions in the income statement.


  • Too much credit spread leads to an interest rate that exceeds the market interest rate, the condition the margin contribution required to cover default risks cannot be implemented on the market.