Date: Thu, 28 Mar 2024 20:28:35 +0100 (CET)
Message-ID: <1195088923.21.1711654115034@vm-confFLEXde>
Subject: Exported From Confluence
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Maschinelles Lernen ist ein Oberbegriff f=C3=BCr die "k=C3=BCnst=
liche Erzeugung von Wissen aus Erfahrung=E2=80=9C.
Ein k=C3=BCnstliches=
System lernt auf der Grundlage von Beispielen und kann diese nach Abschlus=
s der Lernphase verallgemeinern.
Bisher bietet FlexFinance die Nutzung von Ergebnissen, die in neuronalen=
Netzwerken generiert werden, f=C3=BCr:
FlexFinance verwendet einen zweistufigen Ansatz bei der Umsetzung der ne=
uronalen Netzwerke:
- Trainieren des neuronalen Netzwerks auf Portfolioebene
- Anwendung des trainierten neuronalen Netzwerks auf Einzelgesch=C3=A4fts=
ebene
Das maschinelle Lernen in FlexFinance konzentriert sich auf die Berechnu=
ng der Ausfallwahrscheinlichkeit, die Verlustquote bei Ausfall und die erwa=
rteten Kreditverluste. Die Ausfallwahrscheinlichkeit und die Verlustquote b=
ei Ausfall finden ihre Anwendung wieder in einer Methode f=C3=BCr die Berec=
hnung des credit spread.
Die folgenden Aspekte sind f=C3=BCr jedes neuronale Netzwerk releva=
nt:
- Eingabeparameter (Daten, die beim Training des neuronalen Netzes ber=C3=
=BCcksichtigt werden)
- Aufbau und Training des neuronalen Netzes
- Ausgabe z. B. =E2=80=9EEarly warning=E2=80=9C
Eingabeparameter f=C3=BCr das neuronale Netz sind in Da=
ta-Marts organisiert. Die Daten betreffen:
- das Kundenprofil (Beruf, Anzahl der Kinder, Eigentum usw.)
- Historie der Gesch=C3=A4fte und der zugeh=C3=B6rigen Zahlungen (z. B. =
=C3=BCberf=C3=A4llige Tage, ausstehende Zahlungen zu bestimmten Stichtagen)=
- makro=C3=B6konomische Parameter
- aktuelle vertragliche Gesch=C3=A4ftsdaten (Ableitung der Ausfallkredith=
=C3=B6he)
Bez=C3=BCglich des neuronalen Netzwerks k=C3=B6nnen:
- Parameter zwecks Kalkulation festgelegt werden
Abbildung: Konfiguration der Paramete=
r f=C3=BCr das neuronale Netz
- Das Training des neuronalen Netzes kann gestartet werd=
en
f=C3=BCr das aktuelle Buchungsdatum, unter Verwendung der letzten Da=
teneingabe f=C3=BCr die zugrunde liegenden Data Marts. F=C3=BCr jede Traini=
ngsiteration wird ein Test gegen die Trainingsgruppe durchgef=C3=BChrt.&nbs=
p;Der Test vergleicht die auf Basis des Trainingsportfolios gemachte Vorher=
sage mit der realen Zahl im Testportfolio.
Die folgende Grafik zeigt d=
en Trainingsablauf: Ein Portfolio wird in zwei Gruppen von Gesch=C3=A4ften =
geteilt, um ein neuronales Netzwerk zu trainieren:
Unter anderem kann die Anzahl der Iterati=
onen =E2=80=9ETrainieren..> Test =E2=86=92 Trainieren=E2=86=92 Test =E2=
=86=92 ....=E2=80=9C als Parameter f=C3=BCr das neuronale Netzwerk konfigur=
iert werden.
Abbildung: Modellwert im Vergleich zu=
Iteration
- Der Einfluss der einzelnen Attribute (Gewicht in %) is=
t das Ergebnis des Trainierens eines neuronalen Netzwerks. Es ist ers=
ichtlich, welche Parameter und welche Gewichtung in die Berechnung der e=
rwarteten Kreditverluste f=C3=BCr das gesamte Portfolio von Aktiva einb=
ezogen wurden.
Abbildung: Trainieren des neuronalen =
Netzwerks auf Portfolioebene
Das maschinelle Lernen nutzt eine hoch performante Datenbank als Grundla=
ge f=C3=BCr das Fr=C3=BChwarnsystem.
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