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.

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

Credit card fraud is increasing considerably with the development of modern technology and the global superhighways of communication. Credit card fraudsters continuously try to come out with a new tactic challenged the present technology and system. It cost both, providers and consumers a lot of money. Thus, quick and accurate model become essential for companies and credit card providers, to decrease their financial and customer trust losses. However, there is a lack of published literature on credit card fraud detection techniques, due to the unlabeled credit card transactions dataset for researchers. High dimensional data refer to data that have multiple variables. The dataset consist of the credit card details, amount transaction, location, time, and personal details of the cardholders that are anonymized. Thus, in this study, a real-world dataset (European Credit card), with PCA transformation applied is being used. The common problem happened in this kind of research is the data tend to be imbalanced. Imbalanced data will often introduce bias which the accuracy of the prediction is not accurate. In this study, the dataset has been train with an oversampling pre-processing technique called SAS Sample and various data mining technique such as Random Forest, KNN, Decision Tree, and Logistic Regression. After several trials, we found out that the regression technique has the best performance among the others.

/pdf/predicting-credit-card-fraud-on-a-imbalanced-data-2vhbej0mal.pdf

Predicting Credit Card Fraud on a Imbalanced Data

Majority vote ensemble classifier for accurate detection of credit card frauds

Purpose – The best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS)
algorithm. But the time and cost of this algorithm are high. Using asexual reproduction optimization (ARO) algorithm,
the authors achieved better results in less time. So the authors achieved less cost in a shorter time. Their framework
addressed the problems such as high costs and training time in credit card fraud detection. This simple and effective
approach has achieved better results than the best techniques implemented on our dataset so far. The purpose of this
paper is to detect credit card fraud using ARO.
Design/methodology/approach – In this paper, the authors used ARO algorithm to classify the bank transactions
into fraud and legitimate. ARO is taken from asexual reproduction. Asexual reproduction refers to a kind of production in
which one parent produces offspring identical to herself. In ARO algorithm, an individual is shown by a vector of variables.
Each variable is considered as a chromosome. A binary string represents a chromosome consisted of genes. It is supposed
that every generated answer exists in the environment, and because of limited resources, only the best solution can remain
alive. The algorithm starts with a random individual in the answer scope. This parent reproduces the offspring named bud.
Either the parent or the offspring can survive. In this competition, the one which outperforms in fitness function remains
alive. If the offspring has suitable performance,it will be the next parent, and the current parent becomes obsolete.Otherwise,
the offspring perishes, and the present parent survives. The algorithm recurs until the stop condition occurs.
Findings – Results showed that ARO had increased the AUC (i.e. area under a receiver operating characteristic (ROC)
curve), sensitivity, precision, specificity and accuracy by 13%, 25%, 56%, 3% and 3%, in comparison with AIS,
respectively. The authors achieved a high precision value indicating that if ARO detects a record as a fraud, with a high
probability, it is a fraud one. Supporting a real-time fraud detection system is another vital issue. ARO outperforms AIS
not only in the mentioned criteria, but also decreases the training time by 75% in comparison with the AIS, which is a
significant figure.
Originality/value – In this paper, the authors implemented the ARO in credit card fraud detection. The authors
compared the results with those of the AIS, which was one of the best methods ever implemented on the benchmark
dataset. The chief focus of the fraud detection studies is finding the algorithms that can detect legal transactions from
the fraudulent ones with high detection accuracy in the shortest time and at a low cost. That ARO meets all these
demands.

/pdf/credit-card-fraud-detection-using-asexual-reproduction-4560zxirko.pdf

Credit card fraud detection using asexual reproduction optimization

Fraud is a costly business problem which causes every organization to face huge loss. Fraud may lead to risk of financial loss and loss of the confidence of customers and stakeholders of the company. Cyber security teams and internal audit departments of most of the organizations try to monitor such fraudulent activities using traditional rule-based fraud detection systems. However, with the rapid adaptation of online financial transactions, it is more difficult to identify fraudulent activities by static methods and via data analysis. Further, as traditional rule-based fraud detection systems cannot dynamically adjust the rule set based on the behavioral changes of the fraudsters, there is a high possibility of detecting false positive alerts. The aim of this paper is to review selected machine learning techniques where it can be used to develop a fraud detection system which identifies fraudulent activities in financial transactions.

Monetary Transaction Fraud Detection System Based on Machine Learning Strategies

The most acknowledged payment mode is credit card for both disconnected and online mediums in today's day and age. It facilitates cashless shopping everywhere in the world. It is the most widespread and reasonable approach with regards to web based shopping, paying bills, what's more, performing other related errands. Thus danger of fraud exchanges utilizing credit card has likewise been expanding. In the Current Fraud Detection framework, false exchange is recognized after the transaction is completed. As opposed to the current system, the proposed system presents a methodology which facilitates the detection of fraudulent exchanges while they are being processed, this is achieved by means of Behaviour and Locational Analysis(Neural Logic) which considers a cardholder's way of managing money and spending pattern. A deviation from such a pattern will then lead to the system classifying it as suspicious transaction and will then be handled accordingly.

https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042039/pdf

Aman Gulati

Papers

Credit card fraud detection using neural network and geolocation