{"id":2697,"date":"2021-05-11T14:05:07","date_gmt":"2021-05-11T12:05:07","guid":{"rendered":"https:\/\/devstage.bix-consulting.com\/?p=2697"},"modified":"2023-06-16T12:01:20","modified_gmt":"2023-06-16T10:01:20","slug":"vorhersage-von-zahlungsausfaellen","status":"publish","type":"post","link":"https:\/\/teststage.bix-consulting.com\/en\/vorhersage-von-zahlungsausfaellen\/","title":{"rendered":"Prediction of payment failures"},"content":{"rendered":"

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Challenge<\/h4>\n

Ein in europaweit agierendes Unternehmen bietet Tankkarten f\u00fcr seine ca. 250.000 Gesch\u00e4ftskunden an. Das Gesch\u00e4ftsmodell beinhaltet f\u00fcr das Unternehmen ein hohes finanzielles Risiko, da es prozessbedingt in Vorleistung (Kostenabgang vor Zahlung des Endkunden) geht. Die H\u00f6he der Vorleistung betr\u00e4gt pro Monat einen 10-stelligen Betrag! Ziel muss es demnach sein, die Wahrscheinlichkeit eines Zahlungsausfalls zu minimieren.<\/p>\n

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The approach<\/h4>\n

biX Consulting hat auf Basis von KI ein Modell entwickelt, welches die Problematik der Kundenbewertung deutlich optimiert. Im Rahmen von Big Data werden sekundenschnell interne sowie externe Daten zusammengef\u00fchrt, verarbeitet und daraus eine verl\u00e4ssliche Aussage zur Bonit\u00e4t des (Neu)-Kunden getroffen. Mit dieser L\u00f6sung wird die Wahrscheinlichkeit des Ausfallsrisikos elementar reduziert.<\/p>\n

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Challenge<\/h4>\n

Payment defaults by customers not only result in lost revenue, but even in direct losses, as advance payments are not made. The classification of customers into corresponding rating classes is mostly based on external data sources and only leads to limited success.<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=“1″ _builder_version=“4.21.0″ _module_preset=“default“ custom_margin=“||||false|false“ custom_padding=“0px||0px||false|false“ global_colors_info=“{}“ theme_builder_area=“et_body_layout“][et_pb_row _builder_version=“4.21.0″ _module_preset=“default“ custom_padding=“||0px||false|false“ global_colors_info=“{}“ theme_builder_area=“et_body_layout“][et_pb_column type=“4_4″ _builder_version=“4.21.0″ _module_preset=“default“ global_colors_info=“{}“ theme_builder_area=“et_body_layout“][et_pb_post_title title=“off“ meta=“off“ _builder_version=“4.21.0″ _module_preset=“default“ border_radii=“on|20px|20px|20px|20px“ global_colors_info=“{}“ theme_builder_area=“et_body_layout“][\/et_pb_post_title][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=“1″ _builder_version=“4.21.0″ _module_preset=“default“ custom_margin=“||||false|false“ custom_padding=“||0px||false|false“ global_colors_info=“{}“ theme_builder_area=“et_body_layout“][et_pb_row _builder_version=“4.21.0″ _module_preset=“default“ custom_padding=“0px||0px||false|false“ global_colors_info=“{}“ theme_builder_area=“et_body_layout“][et_pb_column type=“4_4″ _builder_version=“4.21.0″ _module_preset=“default“ global_colors_info=“{}“ theme_builder_area=“et_body_layout“][et_pb_text _builder_version=“4.21.0″ _module_preset=“default“ background_color=“RGBA(255,255,255,0)“ custom_padding=“20px|20px|20px|20px|false|false“ hover_enabled=“0″ border_radii=“off|||20px|20px“ global_colors_info=“{}“ theme_builder_area=“et_body_layout“ sticky_enabled=“0″]<\/p>\n

The approach<\/h4>\n

Based on the historical invoice and master data, a payment profile is created for each customer and an algorithm is used to predict whether payment default will occur within the freely defined period. Based on this model, the required securities, payment methods, etc. of the customer are optimized to then calculate the statistically determined risk.<\/p>\n

By using the biX AI Tools, the solution approach can be fully integrated into your SAP system.<\/p>\n

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Your benefit<\/h4>\n

The risk of payment defaults is noticeably reduced, and direct losses can be avoided. In addition, the system provides a customer rating that can be checked and traced by the finance department and auditors\/regulators.<\/span><\/p>\n

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