Fraud Scoring

The fraud scoring system often has predefined risk rules that analyze user data and assess the risk level of each action. For example, if a new user registers from a high-risk country, the fraud scoring system will flag them as “high risk” and show a weight-based score. The fraud scoring software can also analyze user-submitted data, such as a phone number, and system-gathered data, like an IP address. 

If the user is using a fraudulent proxy or their IP address comes from a spam blacklist, their fraud score will be higher. IPs are used as a valuable data point among Trust and Safety teams that are responsible for ensuring safety on various online platforms. However, a decision to block a user, for example, can’t be made solely due to their IP. Other data points need to be checked to make an informed decision. Sometimes, reverification can be triggered, or the user might be asked to provide additional data. 

Apart from IP addresses, information like the user’s name, billing address, location, device, transaction history, and other historical data, such as being involved in other suspicious transactions or having the same address of a known fraudster, are indications of potential crime, negatively impacting the user’s overall fraud score. 

A custom rule in fraud scoring is a tailored rule that’s based on specific criteria that the business using the software wants to acknowledge. Assigning point values to each rule helps determine a customer’s overall fraud risk score. Different from the predefined rules, custom options like this are good when the business operates in a niche or wants to reduce false positives by customizing the risk factors based on its particular use case and industry. 

Fraud scoring improves fraud detection capabilities. Since it’s automated, the system helps businesses increase efficiency in decision making, simplifying processes like risk assessment. By combining multiple rules with machine learning, fraud scoring reduces false positives. Internal compliance teams and fraud prevention specialists who are responsible for managing the risk scoring software can use custom rules and automation to adjust risk categories and detect AML red flags faster. This helps save time and focus on enhanced due diligence (EDD) measures and high-priority cases where manual review is necessary.