Fraud impacts banks that provide online payments service and challenges audit companies and financial players. Driven by growing B2C online trade, an increasing number of credit card transaction evaluations and credit decisions have to be made in real time. Manual processes won’t scale and are error prone. The challenge is to implement real-time transaction assessment and improve the accuracy of fraud detection. The overall objective is to reduce decision cycles, improve customer experience, automate the transaction and lending process, to recognize fraudulent transactions and transactions involving a credit line that won’t be covered later on. This case study will provide an example application of behavioral fraud analytics to credit card transactions data provided by YapiKredi. Based on roughly 30 million transactions this case study will present a technical deep dive comparing behavioral approaches in combination with machine learning against purely statistical/machine learning approaches.