English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

There are many studies that have applied data mining to banking. However, the lack of proper data mounts a serious obstacle to the employment of data mining techniques by banks. This paper examines previous data mining research in the field of banking to extract all served entities and attributes required for analytical purposes, categorize these attributes and ultimately present a data model for analysis. After analyzing a wide range of data mining applications in banking, 28 entities with 423 attributes were identified and the final proposed entity-relationship model was drawn. Also, a checklist was provided based on the model for auditing data gap in banks and applied to a real case. The results of this paper can be seen as a supportive tool for improving bank‘s business intelligence maturity from the data perspective and enabling managers for analyzing data requirement of information systems. Subject Categories and Descriptors [H.2.8 Database Applications]; Data mining: [D.3.3 Language Constructs and Features]; Data types and structures General Terms: Data Mining, Banking Data, Data Analysis

A Study of Data Requirements for Data Mining Applications in Banking