# Credit Card Fraud Detection in Card-Not-Present Transactions: Where to Invest?
## 1 Introduction  
According to Statista [1], global retail e-commerce will reach almost 7 k billion US dollars in 2023 (Figure 1). E-commerce heavily relies on credit cards as a means of payment and credit card adoption, and the number of credit card transactions grows accordingly. Sadly, it seems that fraudsters are keeping track and even thriving in this growing environment. The credit card fraud ratio is the same or perhaps slightly growing (note the green line in Figure 2). Though credit card fraud percentages are seemingly small (around 0.04%), the losses are staggering in absolute numbers. For instance, the annual value of losses on card-not-present(CNP) fraud for debit and credit cards issued in the United Kingdom (UK) alone for 2019 amounted to 470.2 million GBP [2].  
Over the years, technology has significantly changed, and so have the fraud patterns. Today, CNP is a dominant type of fraud, as visible in Figure 2, and it is reported that [3]: \"CNP fraud accounted for \(\xi\)1.43 billion in fraud losses in 2018 (an increase of 17.7% compared with 2017).\" This share has been growing steadily since 2008 (not displayed in the chart). For that reason, in this paper, we focused on CNP transactions alone.  
Our task was to consider how to efficiently enhance existing real-world credit card processing infrastructure using data mining techniques while at the same time considering the practical implications.  
Credit card transactions must satisfy two conflicting properties from a technical standpoint: they must be fast (measured in milliseconds), and they must be secure. This is why credit card transaction processing is performed in multiple stages: real-time, nearreal-time, and offline [4]. In real-time, only fundamental checks are performed (PIN, balance,... ), followed by near real-time, where traditionally rule engines are used.  
Figure 3 shows simplified authorization and fraud detection flow.  
Figure 1: Retail e-commerce sales worldwide from 2014 to 2023 (in billion U.S. dollars).  
Figure 2: Evolution of the total value of card fraud using cards issued within Single Euro Payments Area.  
Rule engines validate transactions against a set of manually defined rules and are probably still dominant in production systems (this information is hard to confirm as credit card processors understandably tend to be secretive about their inner workings). In recent years, rule engines are complemented with various machine learning models to boost the overall fraud detection precision. A significant number of papers have been published in the last ten years on the subject [4; 5] such as [6; 7; 8]. However, it is hard to say to what extent this trend penetrated the production systems, which are very conservative and secretive. Rule engines have the lovely property of being interpretable, which cannot be said for most data mining models. Finally, the final verdict on the fraud case is given \"offline\" by a human expert having all the relevant information at his or her disposal. In this paper, using a real-world dataset in cooperation with our industrial partner, we address credit card fraud detection challenges to produce a proper business plan: where best to invest time and money considering the usual budgetary and time constraints. In other words, we perform triage of sorts, gaining insights that might generalize well to similar datasets and fraud detection problems. We contribute by studying this problem from the wider, business perspective (on how to build a fraud detection system efficiently and practically), from the architectural perspective (which relates to the scalability), and by corroborating findings in the literature pertaining to the algorithm selection and feature engineering.