Object

Title: Application of business intelligence methods for analyzing a loan dataset

Creator:

Vasiliev, Julian ; Stoyanova, Miglena ; Stancheva, Emiliya

Description:

Informatyka Ekonomiczna = Business Informatics, 2018, Nr 1 (47), s. 97-106

Publisher:

Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu

Place of publication:

Wrocław

Date:

2018

Resource Type:

artykuł

Resource Identifier:

doi:10.15611/ie.2018.1.08 ; oai:dbc.wroc.pl:63306

Language:

eng

Relation:

Informatyka Ekonomiczna = Business Informatics, 2018, Nr 1 (47)

Location:

Uniwersytet Ekonomiczny we Wrocławiu

Title in english:

Zastosowanie metod business intelligence do analizy danych pożyczkowych

Abstrakt:

The application of business intelligence methods is usually oriented on a specific type of business. This article is focused on the business intelligence data analysis of a sample loan dataset. There are a lot of well-known methods for analysing loan datasets. The research aim is to find some dependencies in a sample loan dataset which are not visible using techniques like sorting, filtering and pivoting. The following software products are used: Alyuda Neurointelligence and MS Power BI. As a result of the analysis it is proved that the amount of a loan depends mainly on the “born in the region” and “gender” factors. The analysis of the loan dataset in Alyuda Neurointelligence (using Input Importance) shows that the factor “born in the region” is the most important one affecting the amount of a loan. But the input importance shows only the possible strength of the influence of various factors on the amount of the loan and not the direction of their influence. The direction of their influence is found by applying the one-way ANOVA method in PSPP. The analysis of the loan dataset in Power BI shows some interesting dependencies. For debtors born outside the region, the difference in total loan amounts rises to over 4.5 times in favour of men. The average loan amount of debtors who were born outside the region is about 2.5 times greater than the average loan amount of debtors born in the region

Access Rights:

Dla wszystkich zgodnie z licencją

License:

CC BY-NC-ND 3.0 PL

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