← Search

Data · dataset · 2022

Fraud Exploitation

Listed in Borealis

The purpose of this dataset is to build machine learning classifiers to predict exploitation of securities fraud victims in Canada.

Description

The dataset consists of numeric, float, and categorical variables. The dataset also consist of features related to victims’ demographics, financial profile, and investments.

This work was done with funding from a SSHRC Insight Development Grant

Links

Where it is published

Catalogue records · 1

Topics

Stated by source
Business and Management · Law
Provenance · 1 source records, 13 field assertions
SourceKeyLast seenRaw
Borealisdoi:10.5683/SP3/UMZH4V13 d agoJSON v1
FieldAssertionExtractorEvidence
concepts[field].anzsrc:group:4602mapping · borealisdata cavocabulary-mapper@1.0.0keywords['artificial intelligence']
concepts[field].anzsrc:group:4611mapping · borealisdata cavocabulary-mapper@1.0.0keywords['machine learning']
concepts[field].dataverse_subject:business-and-managementsource · borealisdata caconnector:borealisdata_ca@1.0.0/subjects
concepts[field].dataverse_subject:lawsource · borealisdata caconnector:borealisdata_ca@1.0.0/subjects
concepts[field].local:field:computer-science-aimapping · borealisdata caconnector:borealisdata_ca@1.0.0/subjects
concepts[field].local:field:economics-financemapping · borealisdata caconnector:borealisdata_ca@1.0.0/subjects
concepts[field].local:field:social-sciencemapping · borealisdata caconnector:borealisdata_ca@1.0.0/subjects
created_datesource · borealisdata caconnector:borealisdata_ca@1.0.0
descriptionsource · borealisdata caconnector:borealisdata_ca@1.0.0/description
publication_datesource · borealisdata caconnector:borealisdata_ca@1.0.0
titlesource · borealisdata caconnector:borealisdata_ca@1.0.0/name
updated_datesource · borealisdata caconnector:borealisdata_ca@1.0.0
version_labelsource · borealisdata caconnector:borealisdata_ca@1.0.0