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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we investigate the extent to which features derived from bankstatements provided by loan applicants, and which are not declared on anapplication form, can enhance a credit scoring model for a New Zealand lendingcompany. Exploring the potential of such information to improve credit scoringmodels in this manner has not been studied previously. We construct a baselinemodel based solely on the existing scoring features obtained from the loanapplication form, and a second baseline model based solely on the new bankstatement-derived features. A combined feature model is then created byaugmenting the application form features with the new bank statement derivedfeatures. Our experimental results using ROC analysis show that a combinedfeature model performs better than both of the two baseline models, and showthat a number of the bank statement-derived features have value in improvingthe credit scoring model. The target data set used for modelling was highlyimbalanced, and Naive Bayes was found to be the best performing model, andoutperformed a number of other classifiers commonly used in credit scoring,suggesting its potential for future use on highly imbalanced data sets.

Improving a Credit Scoring Model by Incorporating Bank Statement Derived Features