# Credit Card Fraud Detection in Card-Not-Present Transactions: Where to Invest?
## 3 Related Work  
Various approaches have been proposed to solve the issue of detecting fraud in financial transactions. Traditional fraud prevention mechanisms in banks are mostly based on manpower-based rules such as the one presented in [38]. The rules describe the circumstances of a financial transaction that is considered suspicious and potentially worth checking. Rule-based solutions are flexible, but also challenging and time-consuming to implement and maintain as it requires the diligent definition of every single rule for some possible anomaly. If an expert fails to define a suspicious situation, undetected anomalies will happen, and nobody will be aware of them. On the other hand, as time passes by and credit card fraud patterns change, some rules become obsolete. This imposes the need to periodically assess the usefulness of a rule within a set of rules and decide whether to keep or drop the rule from the pool. The typical development of a fraud detection system starting with the application of a rule-based approach that initially worked well is described in [9]. The paper describes (which is consistent with our industry partners' experience) that as the ruleset increases, the effort to maintain a transaction monitoring system also increases, and consequently, the accuracy of fraud detection decreases. An interesting approach that assigns a normalized score to the individual rule, quantifying the rule influence on the pool's overall performance, is described in [39]. To improve detection and mitigate the limitations of rule-based systems, fraud detection solutions employ machine learning techniques divided into supervised and unsupervised [40]. In supervised techniques, models developed using annotated samples of fraudulent and non-fraudulent transactions are used to classify transactions as fraudulent or genuine. In contrast, unsupervised techniques seek those accounts, customers, transactions, etc., which raise doubts by differing significantly from most data. The recognition of such unalike data is called anomaly detection or outlier detection. A lot of outlier detection algorithms have been proposed in the literature, many of them being cluster-based [41; 42; 43; 44]. Contrary to supervised approaches, unsupervised outlier detection does not require annotated transactions and can detect unforeseen outlying cases. The uncertainty factor in unsupervised techniques is that we do not have the annotated set to compare them with and are unsure about the results. Since banks have a vast number of high-quality fraud-labeled data records, supervised methods prevail. Neural networks have been used to detect fraud due to their huge popularity in the 1990s (e.g., [45; 46]) and now again, with the advent of deep learning (e.g., [47; 48]), but practically \"all\" machine learning algorithms were tested against this problem, with logistic regression [49; 50; 51; 52], SVM [49; 50; 51; 53; 54], and random forests [55; 56; 57; 49; 50; 52] being the most popular ones. Other supervised approaches include decision trees [53; 56; 57; 58; 59], Bayesian models [7; 41; 57; 58; 60], association rules [61], Hidden Markov model [8], etc. The techniques mentioned differ in their computational complexity. Some, like neural networks and Bayesian networks, are intrinsically computationally intensive while, for example, for the K-nearest neighbor algorithm, computation time is usually very low [49]. Another critical aspect of the techniques used is the ability to support the well-known concept of drift problem. A supervised model must be periodically retrained to address concept drift. [PERSON] and [PERSON] showed in [8] that most techniques do not directly address concept drift. Unaddressed or poorly addressed concept drift leads to sporadic updates of the fraud detection model, resulting in periods of poor-quality fraud detection. Ensemble (hybrid) models combining multiple distinct models have been the \"gold standard\" lately.