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.
Highlights
Frauds and unpredictable defaults cost billions of dollars each year, forcing financial institutions to continuously improve their systems for loss reduction and, consequentially, fraud detection and credit scoring became hot spots to explore and, in the past twenty years, a large amount of studies have proposed the use of novel data mining techniques for fraud detection, credit scoring and risk management
This paper reviewed the literature describing use of the fraud detection and credit scoring approaches based on supervised, unsupervised, semi-supervised, ensemble and transfer techniques
It is noticed that most fraud detection systems employ at least one supervised learning method
Summary
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 to some specific firms, but to industries and governments worldwide. The explosive growth of China’s credit market provides opportunities for related organizations to make profit, for customers to get bland new services and products, for fraudsters to hunt for unauthorized benefits, and for researchers to design intelligent fraud detection and credit scoring systems. 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.
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