A state of the art survey of data mining-based fraud detection and credit scoring

Abstract

Abstract.Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches.

Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

“…Table 10 presents a comparative summary of seven relevant SLRs and surveys performed in the area of fraud detection, including our contribution. In the "Context" column of Table 10, there are four SLRs that are exclusively related to some aspect of data mining [25,26,28,29], while only one is related to some aspect of fraud theory [75], in addition to other approaches [73,74]. The last row of Table 10 also presents information about the SLR covered in this document, the context of which explores both data mining and fraud theories together, unlike the other seven presented in this table.…”

“…Zhou et al [26] concluded that most fraud-detection systems employ at least one supervised learning method and that unsupervised and semi-supervised learning methods are also used. The study showed that these techniques can be used alone or in combination to build more robust classifiers and that, without losing generality, these approaches are relatively successful in detecting fraud and credit scoring.…”

Unsupervised Anomaly Detection of Healthcare Providers Using Generative Adversarial Networks

“…The LR, XGB, RF and DT algorithms are successful in detecting anomalies [6,18,22] however their main use in the current study is their ability to generalize, feature selection, interpretability [6,18,22,30] and further explain how the features contribute to the anomalous healthcare providers. This explanation was further achieved through the use of SHapley Additive exPlanation (SHAP) [13].…”

Graph-Based Fraud Detection with the Free Energy Distance

“…Common unsupervised methods include standard clustering methods, self-organizing map [39] and peer group analysis [37]. The interested reader is advised to consult [10,26,41] for more information.…”

“…The last category of classification techniques is semi-supervised learning which lies between supervised and unsupervised techniques, since it constructs predictive models using labeled samples together with a usually larger amount of unlabeled samples [13]. Some common semi-supervised methods are graph-based approaches, which consist in the creation of a graph model that reflects the relations included in the data and then transfers the labels on the graph to build a classification model [41]. Compared to the two other categories presented before, there are few publications about semi-supervised methods applied to card fraud detection.…”