How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark

Payment card fraud leads to heavy annual financial losses around the world, thus giving rise to the need for improvements to the fraud detection systems used by banks and financial institutions. In the academe, as well, payment card fraud detection has become an important research topic in recent years. With these considerations in mind, we developed a method that involves two stages of detecting fraudulent payment card transactions. The extraction of suitable transactional features is one of the key issues in constructing an effective fraud detection model. In this method, additional transaction features are derived from primary transactional data. A better understanding of cardholders’ spending behaviors is created by these features. After which the first stage of detection is initiated. A cardholder's spending behaviors vary over time so that new behavior of a cardholder is closer to his/her recent behaviors. Accordingly, a new similarity measure is established on the basis of transaction time in this stage. This measure assigns greater weight to recent transactions. In the second stage, the dynamic random forest algorithm is employed for the first time in initial detection, and the minimum risk model is applied in cost-sensitive detection. We tested the proposed method on a real transactional dataset obtained from a private bank. The results showed that the recent behavior of cardholders exerts a considerable effect on decision-making regarding the evaluation of transactions as fraudulent or legitimate. The findings also indicated that using both primary and derived transactional features increases the F-measure. Finally, an average 23% increase in prevention of damage (PoD) is achieved with the proposed cost-sensitive approach.

Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors

Credit card fraud has emerged as major problem in the electronic payment
sector. In this survey, we study data-driven credit card fraud detection
particularities and several machine learning methods to address each of its
intricate challenges with the goal to identify fraudulent transactions that
have been issued illegitimately on behalf of the rightful card owner. In
particular, we first characterize a typical credit card detection task: the
dataset and its attributes, the metric choice along with some methods to handle
such unbalanced datasets. These questions are the entry point of every credit
card fraud detection problem. Then we focus on dataset shift (sometimes called
concept drift), which refers to the fact that the underlying distribution
generating the dataset evolves over times: For example, card holders may change
their buying habits over seasons and fraudsters may adapt their strategies.
This phenomenon may hinder the usage of machine learning methods for real world
datasets such as credit card transactions datasets. Afterwards we highlights
different approaches used in order to capture the sequential properties of
credit card transactions. These approaches range from feature engineering
techniques (transactions aggregations for example) to proper sequence modeling
methods such as recurrent neural networks (LSTM) or graphical models (hidden
markov models).

Credit card fraud detection using machine learning: A survey

commerce becomes popular for online shopping, banking, financial institution and government. Fraudulent activity exits in many areas of businesses and our daily life. Such activities are most prevalent in telecommunication, credit card fraud detection, network intrusion, finance and insurance and scientific applications, Billions of dollars are loss every day due to the increase in the number of credit card transactions by using online as well as offline. To design potent and efficient fraud detection algorithms is the key for reducing the losses in transaction. There were numerous approaches have been implemented for the fraud detection. This paper shows the approaches used in fraud detection in e-commerce.

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A Survey on Fraud Detection Techniques in Ecommerce

The data mining technology is used to identifying patterns and information from a huge quantity of data In a single repository data base where data is stored in central site, then applying data mining algorithms on these data base, patterns are extracted, which is clearly implausible and untenable for many realistic problems and databases To deal with these complex systems has revealed opportunities to improve distributed data mining systems in a number of ways Furthermore, in certain situations, data may be inherently distributed and cannot be merged into a single database for a variety of reasons including security, fault tolerance, legal constraints, competitive reasons, etc In such cases, it may not be possible to examine all of the data at a central processing site to compute a single global model Here, we develop techniques that scale up to large and possibly physically distributed databases Meta-learning (learning from learned knowledge) – a technique dealing with the problem of computing a global classifier from large and inherently distributed databases This paper, describes meta-learning and JAM system (Java Agents for Metalearning), which is an agent-based meta-learning system for large-scale data mining applications Several important desiderata of data mining systems are addressed (ie, scalability, efficiency, portability, compatibility, adaptivity, extensibility and effectiveness) and a combination of AI-based methods and distributed systems techniques are presented We applied JAM on the real-world data mining task of modeling and detecting credit card fraud with notable success

Agent based meta learning in distributed data mining system

￻ Abstract — The most accepted payment mode is credit card for both online and offline in today's world, it provides cashless shopping at every shop in all countries. It will be the most convenient way to do online shopping, paying bills etc. Hence, risks of fraud transaction using credit card has also been increasing. In the existing credit card fraud detection business processing system, fraudulent transaction will be detected after transaction is done. It is difficult to find out fraudulent and regarding loses will be barred by issuing authorities. Hidden Markov Model is the statistical tools for engineer and scientists to solve various problems. In this paper, it is shown that credit card fraud can be detected using Hidden Markov Model during transactions. Hidden Markov Model helps to obtain a high fraud coverage combined with a low false alarm rate.

Shailesh S. Dhok

Papers

Credit Card Fraud Detection Using Hidden Markov Model