Using hidden Markov models in credit card transaction fraud detection

In this paper we shall propose a credit card transaction fraud detection framework which uses Hidden Markov Models, a well established technology that has not as yet been tested in this area and through which we aim to address the limitations posed by currently used technologies. Hidden Markov Models have for many years been effectively implemented in other similar areas. The flexibility offered by these models together with the similarity in concepts between Automatic Speech Recognition and credit card fraud detection has instigated the idea of testing the usefulness of these models in our area of research. The study performed in this project investigated the utilisation of Hidden Markov Models by means of proposing a number of different frameworks, which frameworks are supported through the use of clustering and profiling mechanisms. The profiling mechanisms are used in order to build Hidden Markov Models which are more specialised and thus are deployed on training data that is specific to a set of cardholders which have similar spending behaviours. Clustering techniques were used in order to establish the association between different classes of transactions. Two different clustering algorithms were tested in order to determine the most effective one. Also, different Hidden Markov Models were built using different criteria for test data. The positive results achieved portray the effectiveness of these models in classifying fraudulent and legitimate transactions through a resultant percentage value which indicates the prominence of the transaction being contained in the respective model.peer-reviewe

Comparative Analysis of Different Distributions Dataset by Using Data Mining Techniques on Credit Card Fraud Detection

Banks suffer multimillion-dollars losses each year for several reasons, the most important of which is due to credit card fraud. The issue is how to cope with the challenges we face with this kind of fraud. Skewed "class imbalance" is a very important challenge that faces this kind of fraud. Therefore, in this study, we explore four data mining techniques, namely naïve Bayesian (NB),Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF), on actual credit card transactions from European cardholders. This paper offers four major contributions. First, we used under-sampling to balance the dataset because of the high imbalance class, implying skewed distribution. Second, we applied NB, SVM, KNN, and RF to under-sampled class to classify the transactions into fraudulent and genuine followed by testing the performance measures using a confusion matrix and comparing them. Third, we adopted cross-validation (CV) with 10 folds to test the accuracy of the four models with a standard deviation followed by comparing the results for all our models. Next, we examined these models against the entire dataset (skewed) using the confusion matrix and AUC (Area Under the ROC Curve) ranking measure to conclude the final results to determine which would be the best model for us to use with a particular type of fraud. The results showing the best accuracy for the NB, SVM, KNN and RF classifiers are 97,80%; 97,46%; 98,16% and 98,23%, respectively. The comparative results have been done by using four-division datasets (75:25), (90:10), (66:34) and (80:20) displayed that the RF performs better than NB, SVM, and KNN, and the results when utilizing our proposed models on the entire dataset (skewed), achieved preferable outcomes to the under-sampled dataset

Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection

Gaining the trust of customers and providing them empathy are very critical in the financial domain. Frequent occurrence of fraudulent activities affects these two factors. Hence, financial organizations and banks must take utmost care to mitigate them. Among them, ATM fraudulent transaction is a common problem faced by banks. There following are the critical challenges involved in fraud datasets: the dataset is highly imbalanced, the fraud pattern is changing, etc. Owing to the rarity of fraudulent activities, Fraud detection can be formulated as either a binary classification problem or One class classification (OCC). In this study, we handled these techniques on an ATM transactions dataset collected from India. In binary classification, we investigated the effectiveness of various over-sampling techniques, such as the Synthetic Minority Oversampling Technique (SMOTE) and its variants, Generative Adversarial Networks (GAN), to achieve oversampling. Further, we employed various machine learning techniques viz., Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting Tree (GBT), Multi-layer perceptron (MLP). GBT outperformed the rest of the models by achieving 0.963 AUC, and DT stands second with 0.958 AUC. DT is the winner if the complexity and interpretability aspects are considered. Among all the oversampling approaches, SMOTE and its variants were observed to perform better. In OCC, IForest attained 0.959 CR, and OCSVM secured second place with 0.947 CR. Further, we incorporated explainable artificial intelligence (XAI) and causal inference (CI) in the fraud detection framework and studied it through various analyses.Comment: 34 pages; 21 Figures; 8 Table

Detecting money laundering using hidden Markov model

Recent money laundering scandals, like the Danske Bank and Swedbank’s failure to mitigate money laundering risks (Kim, 2019), have made “anti money laundering” (AML) a much discussed topic. Governments are making AML regulations tougher and financial institutions are struggling to comply, one of the requirements is to actively monitor financial transactions to detect suspicious ones. Most of the financial industry applies simple rule-based methods for monitoring. This thesis provides a practical model to detect suspicious transactions using the hidden Markov model (HMM). The use of HMM is justified, because the criminal nature of a transaction is hidden to the financial institution, only transaction parameters can be observed. By using past data, a model is built to detect if current transaction is suspicious or not. The model is assessed with artificial and real transactions data. It was concluded that this model performs better than a classical k-means clustering algorithm

A Framework for Discovery and Diagnosis of Behavioral Transitions in Event-streams

Date stream mining techniques can be used in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment. When the quality of some data mined models varies significantly from nearby models—as defined by quality metrics—then the user’s behavior is automatically flagged as a potentially significant behavioral change. Decision tree, sequence pattern and Hidden Markov modeling being used in this study. These three types of modeling can expose different aspect of user’s behavior. In case of decision tree modeling, the specific changes in user behavior can automatically characterized by differencing the data-mined decision-tree models. The sequence pattern modeling can shed light on how the user changes his sequence of actions and Hidden Markov modeling can identifies the learning transition points. This research describes how model-quality monitoring and these three types of modeling as a generic framework can aid recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The date stream mining techniques mentioned are used to monitor patient goals as part of a clinical plan to aid cognitive rehabilitation. In this context, real time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. This generic framework can be widely applicable to other real-time data-intensive analysis problems. In order to illustrate this fact, the similar Hidden Markov modeling is being used for analyzing the transactional behavior of a telecommunication company for fraud detection. Fraud similarly can be considered as a potentially significant transaction behavioral change