There are a number of fraud detection tools available on the market, but for some companies, it’s important to have greater control over how the fraud scoring algorithms work and to have them keep learning and improving using machine learning. Our team has put together a demo and some sample code to show how you can build this with Fuzzy.ai.
For this example, let’s assume that Fuzzy.ai is being used by a company with an online store, where customers order products online. After running their business for a year, they’ve noticed that all of the following factors often indicate that a transaction is fraudulent:
- Transactions from new users
- Transactions much larger than the online store’s average size
- Mismatches and very large distances between the billing address, shipping address, phone number location and IP address location
They also noticed that in addition to the points above, some lesser indicators of fraud are:
- Using the most expensive shipping option might indicate fraud
- Multiple credit cards used from same IP increases fraud
- Multiple credit cards shipping to the same address increases fraud
With Fuzzy.ai, you could quickly build an agent based on these rules and get a fraud score in real-time for each transaction. In fact, we built one and created a demo interface you could use to try it out:
Visit our Fraud Detection demo site
We’ve also made all of the code behind this demo available. Check it out on GitHub.
