Use Upgini for Fraud Prevention

Credit fraud is the criminal use of someone else's personal credentials, as well as their credit standing, to borrow money or use credit cards to purchase goods or services with no intention of repaying the debt.

Upgini uses several user IDs, such as phone number, IP address, and email, to compare the behavioral profile of users for each ID. This helps to find contradictions and increase the quality of your own fraud detection system. Upgini adds 5 to 10 percentage points of accuracy to the average fraud detection system.

Search for new relevant features for your custom Credit Fraud prevention system:

1. Obtain a training dataset of Credit Fraud with customer ID and label. Upgini supports the following customer IDs: phone number, IP address, and email. The label is a mark of Fraud credit application or not.
2. Search for new relevant features for your task: launch FIT method of Upgini and get a list of relevant features from connected data sources.
3. Make a test enrichment: launch TRANSFORM method of Upgini and enrich your dataset with only new relevant features.
4. Use Upgini in production: Upgini moves relevant features for the all Phone numbers, Emails and IP-addresses to your DWH. You may use this data directly in your DWH.

If you don't have your own Credit Fraud prevention system or you don't have a training dataset, you may use the ready Upgini Fraud Score. This is an informational tool that helps you gauge the risk involved with an loan applications before processing. This is done by identifying traits and historical trends associated with suspicious behavior and fraudulent credits.

Ad fraud is any attempt to defraud advertisers for financial gain. Scammers often use bots to carry out ad fraud. Scammers could generate fake leads for you that could never be contacted.

Upgini can help you arbitrage leads from external systems. It can score leads and determine which lead is fake, so you don't waste time and money contacting it.

Search for new relevant features that could help you determine whether a lead is fake:

1. Obtain a training dataset for determining fake leads that includes customer ID and label. Upgini supports the following customer IDs: phone number, IP address, and email. The label indicates whether a lead if fake or not.
2. Search for new relevant features for your training dataset: launch the FIT method of Upgini and receive a list of relevant features from connected data sources.
3. Make a test enrichment: launch the TRANSFORM method of Upgini and enrich your dataset with only the new relevant features.
4. Use Upgini in production: Upgini transfers relevant features for all phone numbers, emails, and IP addresses to your DWH. You can use this data directly in your DWH.

If you don't have your own Fake leads detection system or you don't have a training dataset, you may use the ready-made Upgini Fraud Score. This is an informational tool that helps you gauge the risk involved with leads from ads before processing them. This is done by identifying traits and historical trends associated with suspicious behavior and fraudulent contacts that you receive from ads.