Fraud detection has become an important research topic in recent years. In online sales transaction, fraud can occur on a business process. Fraud which occurs on business process is popularly known as process-based fraud (PBF). Previous studies have proposed PBF detection on process business model, however, false decisions are still often made because of new fraud pattern in online sales transactions. False decision mostly occurs since the method cannot identify the attributes of fraud in online sales transaction. This research proposes new fraud attributes and fraud patterns in online transactions. The attributes can be identified by exploring the event logs and Standard Operating Procedure (SOP) of online sales transactions. First, this is conducted by collecting event logs and creating SOP of online sales transaction; then, performing conformance between event logs and SOP; further, discussing with fraud experts about the result of SOP deviations which have been identified; moreover, determining convention value of the SOP deviation to fuzzy value, and classifying the SOP deviation; and at last, establishing fraud attributes and fraud patterns based on classification result. The new fraud attribute and fraud patterns are expected to increase accuracy of fraud detection in online sales transaction. Based on the evaluation, this method resulted a better accuracy 0.03 than the previous one.