Due to recent updates, all users are required to create an Altair One account to login to the RapidMiner community. Click the Register button to create your account using the same email that you have previously used to login to the RapidMiner community. This will ensure that any previously created content will be synced to your Altair One account. Once you login, you will be asked to provide a username that identifies you to other Community users. Email us at Community with questions.
using clustering to check for fraud
mengkoon007
Member Posts: 30 Contributor II
Hi,
I am trying to detect expense claim fraud using rapidminer. I am not too sure what is the suitable modelling technique, thus I tried out k-mean clustering.
I have a huge data containing the following attributes, basically only amount is numeric and from my understanding k-mean can only use to analyze numeric.
- date
- employee
- amount
- expense type
etc
I have done the process and output as below: Basically, I just filter one employee at a time and select the amount attribute.
Qn: How can I analyze from the output to detect if there is any fraud claim?
Thanks.
Tagged:
0
Answers
Fraud is always a great use case but it can be tricky to find them. Have you tried the Anomaly Detection extension? They have a great HBOS score operator.
Or if you already have some identified cases of fraud, then you can create a label and then use some of the supervised machine learning algorithms such as neural nets, random forest, or SVM. All those are popular techniques for fraud detection (assuming you have labeled data).
Lindon Ventures
Data Science Consulting from Certified RapidMiner Experts