Use Upgini for Fraud Prevention

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Registration fraud prevention

Use case description

Registration fraud refers to any customer account created with fraudulent information, a practice that is becoming increasingly difficult to detect as attack techniques rapidly evolve.

Actually, there are a large number and variety of cases, from individuals trying to abuse sales promotions and discount coupons, to highly distributed and sophisticated attacks that use fake or stolen identities, making them difficult to effectively detect and stop.

How Upgini could help with Registration fraud prevention?

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.

How test Upgini for Registration Fraud Prevention?

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

  1. Obtain a training dataset of Registration 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 registration 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.

Fraud Score

If you don't have your own Registration 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 registration applications before processing. This is done by identifying traits and historical trends associated with suspicious behavior and fraudulent registrations.

75% match rate for Phone numbers
40% match rate for Emails
99% match rate for IP-addresses

Prevent fake leads

Use case description

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.

How Upgini could help to find fake leads?

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.

How test Upgini for Fake leads classification?

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.

Fraud Score

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.

75% match rate for Phone numbers
40% match rate for Emails
99% match rate for IP-addresses
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Protection against a competitor's attack

Why does it matter?

There are several methods that competitors may use to attack each other:

  1. Manipulating reviews
  2. Manipulating ordering

These attacks can result in a drop in sales.

How can you solve this problem?

Develop an AI service that will check every order and every review for competitor attack.

How could Upgini help you?

Upgini lets you enrich your systems with new relevant external data that may help you to identify competitor attack by IP-address, user geo position or email: more than 7,000 features are available.

Increase sales
Free testing
Proven commercial effect

Non-pickup orders protection

Why does it matter?

Orders without pickup occupy a significant share of orders in online stores.

Because of this, the store incurs extra logistics costs, does not receive profit from potential sales of blocked goods.

How can you solve this problem?

Score every postpaid order on the probability of pick-up. For orders with a high probability of non-pickup, require prepaid.

How could Upgini help you?

Upgini lets you enrich your systems with new relevant external data that may help you score orders by IP address, user geo position, email, or phone. More than 15,000 features are available to predict whether a customer will pick up an order or not.

Increase sales
Proven commercial effect
Free to test
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Contact Upgini

Feel free to get in touch with us.
We are ready to help you with these cases.