...
The conventional approach for early warning only often works with overdue payment dates (Day days past due). Then, unfortunately, it is often too late to modify and rescue the contract as the loan is usually already in default. At this point, the machine learning approach analyses the possible influences much more extensively and provides alerts before a payment date becomes overdue. In addition, machine learning takes into account not only customer and contract data but also macro- and microeconomic factors that naturally influence payment behaviour.
...
Conventional segmentation and staging is linked to conservative models to derive PD, LGD at portfolio level and to calculate ECLs for regulatory purposes. Calibration is „manual work“ and hence a source for source of errors. The prediction accuracy depends directly on the quality of the manual work during segmentation and calibration. This approach might not be sufficient for the very fine-granular decision of an early warning system. If in this case, specific actions should be triggered at individual customer level to reduce potential future losses caused by deterioration in credit quality, a more finely granular analysis is recommended that takes the specific sensitivity of an entity/customer relation into account.
...