Fraud detection and prevention is always a batter field. Fraudsters will keep finding new ways to game the system. On the other hand, that is a big business opportunity. Let see how crowded the market is:
This is a hand selected list of some notable players in this markets. There are couple interesting observations:
- Machine learning (or AI) is a must-have feature to the product.
- Big data is still a treasure box with big potential.
- Rule based system is still popular (it is old fashion?).
- A hyper system combining rules and AI will be promising.
- Visualization can be helpful and user friendly for customers.
Anyway, here are two nice summaries from seon.io and unfraud.com:
If you work on e-commerce, beside making sure the online payment is smooth, another critical task is to deal with fraud! Fraud shares lots of common characteristics with (information) security. Good guys and bad guys are always fighting endlessly, just like Marvel comics: super heroes vs. super villains.
Credit: Marvel Comics
Most company either builds an in-house solution or use some market available solution. So what is the hard core of a fraud management system?
Before answering this question, maybe we can go through a simple e-commerce checkout flow (you know in reality, it will be much more complex):
In last 5 years, I have worked on three fraud management systems. In a plaintext, we gather all possible “evidences” of fraudsters and try to convict the “crime”. Translate the previous sentence to technical words: a payment transaction comes to a rule engine, it will run bunch of rules at real time, then outputs a decision like reject, approve, review, etc. Based on the configuration and the business model (Merchant on Record or not, etc.) , the payment system will take corresponding action.
To build the rule engine, Drools is a popular choice. Of cause, we can build a similar in-house version too. The key is the rules. Here is a list of some rules:
Any single item above in details can be an individual post. But hope you get some basic ideas.