What is SaaS Fraud Scoring?

SaaS fraud scoring is the practice of assigning a number to the probability that a user action or transaction is fraudulent, based on an analysis of:

• le comportement des utilisateurs
• device characteristics
• IP address
• identity attributes. 

The score usually ranges from 0 to 1, with higher scores indicating higher levels of risk and leading to actions like triggering a second verification step, sending the transaction for review, or just blocking it. 

As SaaS fraud evolves rapidly, so does the need for a SaaS fraud scoring model that is also flexible enough to be updated regularly with new information.

The SaaS fraud scoring process has the following steps:

1. SaaS fraud scoring systems monitor user activities and create signals for each event. 
2. These signals are then processed using both rules and machine learning models. Rules are used to detect known risky patterns, while models are used to detect anomalies and subtle changes in behavior. 
3. Based on these inputs, the system generates a risk score in real time, which decides if the action should be allowed, challenged, or rejected.

Fraud scores are derived from various user data categories such as the: 

• Identification details
• Transaction data
• Device & browser metadata
• IP reputation
• Geo-location
• Behavior signals (the speed of typing or navigation). 

Most systems also incorporate third-party identity verification and information from other networks to identify fraudsters with accounts at other companies. 

Using first- and third-party data raises the level of efficiency in detecting fraud and protects against new fraud methods.

Fraud scoring for SaaS applications:

• help in the detection of fraud
• provide an opportunity to reduce the dependence on manual reviews
• offer quick resolution and so avoids incurring unnecessary costs and missing out on revenue
• give better visibility of fraud patterns and helps in creating an effective response to incidents
• stop high-risk activities, and at the same time, legitimate users are allowed through, which protects revenue and avoids user experience frustration.

Fraud scoring is crucial for SaaS companies since their business model is based on recurring payments, which can be easily manipulated. 

Recurring revenue models are susceptible to:

• account takeovers
• fraude au paiement
• fake signups. 

A comprehensive fraud scoring system can help in protecting onboarding processes, avoid account takeovers and reduce billing fraud without compromising on efficiency. 

It also helps in meeting compliance requirements such as AML and KYC, and enhances brand trust by reducing the number of false declines and security incidents.

Yes, SaaS fraud scoring is typically done in real-time. 

Scoring models are deployed by SaaS companies to instantly assess the risk of individual transactions as they are being processed. 

This practice is particularly crucial during the registration, log-in, and checkout processes, when high levels of risk are typically present.

Fraud scoring accuracy is dependent on the availability of good-quality data.

However, data can be incomplete, outdated, or contradictory. These factors affect the efficiency of the model, and also the ability to detect suspicious activities. Poor data management increases the risk of fraud, compliance risks, and customer anger.

Fraud scoring tools for SaaS applications integrate with les processeurs de paiement, CRMs, identity providers, and analysis tools via d'API and event streams. 

These integrations enable fraud detection to be incorporated into existing processes. Cloud-native systems integrate easily, while legacy systems might require custom integrators. Strong integration capabilities are a prerequisite to real-time decisions and la gestion des fraudes.

Strong challenges in this area include:

• the balance between strong fraud protection and user convenience
• protecting les informations sensibles
• keeping models effective as fraud methods evolve
• slowed implementation due to integration with legacy systems or limited resources