Query: fraud detection in deep learning

title: A deep learning method for fraud detection in financial systems: poster
abstract: Credit card fraud has been a significant challenge ever since forgers have been inventing new ways to steal money. Thus, adaptive fraud detection methods are required to counter the forgers. Deep learning methods have been attractive candidates because of their adaptive nature to detect emerging credit card frauds. In this paper, we propose a deep learning method to adaptively detect credit card frauds by using neural networks. We experimentally evaluated our model with Kaggle dataset. The analyses results show that our methods adaptively detect credit card frauds.

title: HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture
abstract: Credit card transaction fraud costs billions of dollars to card issuers every year. A well developed fraud detection system with a state of the art fraud detection model is regarded as essential to reducing fraud losses. The main contribution of our work is the development of a fraud detection system that employs a deep learning architecture together with an advanced feature engineering process based on homogeneity oriented behavior analysis (HOBA) Based on a real life dataset from one of the largest commercial banks in China, we conduct a comparative study to assess the effectiveness of the proposed framework. The experimental results illustrate that our proposed methodology is an effective and feasible mechanism for credit card fraud detection. From a practical perspective, our proposed method can identify relatively more fraudulent transactions than the benchmark methods under an acceptable false positive rate. The managerial implication of our work is that credit card issuers can apply the proposed methodology to efficiently identify fraudulent transactions to protect customers' interests and reduce fraud losses and regulatory costs.

title: Champion challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning
abstract: Credit card fraud detection is an essential part of screening fraudulent transactions in advance of their authorization by card issuers. Although credit card frauds occur extremely infrequently, they result in huge losses as most fraudulent transactions have large values. An adequate detection of fraud allows investigators to take timely actions that can potentially prevent additional fraud or financial losses. In practice, however, investigators can only check a few alerts per day since the investigation process can be long and tedious. Thus, the primary goal of the fraud detection model is to return accurate alerts with fewer false alarms and missed frauds. Conventional fraud detection is mainly based on the hybrid ensemble of diverse machine learning models. Recently, several studies have compared deep learning and traditional machine learning models including ensemble. However, these studies used evaluation methods without considering that the real world fraud detection system operated with the constraints: (i) the number of investigators who check the high risk transactions from the data driven scoring models are limited and (ii) the two types of misclassification, false alarms and missed frauds, have different costs. In this study, we conducted an in depth comparison between the hybrid ensemble and deep learning method to determine whether or not to adopt the latter in our partner's system that currently operates with the hybrid ensemble model. To compare the two, we introduced the champion challenger framework and the development process of the two models. After developing the two models, we evaluated them on large transaction data sets taken from our partner, a major card issuing company in South Korea. We used various practical evaluation metrics appropriate for this domain that has severe class and cost imbalances. Moreover, we deployed these models in a real world fraud detection system to check the post launch performance for one month. The challenger outperformed the champion on both in off line and post launch tests.

title: Deep Learning Domain Adaptation Techniques for Credit Cards Fraud Detection
abstract: Although the incidence of credit card fraud is limited to a small percentage of transactions, the related financial losses may be huge. This demands the design of automatic Fraud Detection Systems (FDS) able to detect fraudulent transactions with high precision and deal with the heterogeneous nature of the fraudster behavior. Indeed, the nature of the fraud behavior may strongly differ according to the payment system (e.g. e commerce or shop terminal) the country and the population segment. Given the high cost of designing data driven FDSs, it is more and more important for transactional companies to reuse existing pipelines and adapt them to different domains and contexts: this boils down to a well known problem of transfer learning.

title: Credit Card Fraud Detection using Deep Learning
abstract: Changing technology leads to various advancements. Here we talk about online transactions that is by the means of credit cards, which leads to credit card frauds, and this paper is about making a credit card fraud detection system using Deep Learning Neural Networks. Even if the Neural Network is trained over a large number of iterations, it is not accurate enough to classify the data as fraudulent or non fraudulent because of the skewness in the dataset. We use two sampling mechanisms: Under Sampling, by reducing number of non fraudulent observations and Over Sampling, where the fraudulent class observations are duplicated. The results obtained show that these methods increase the accuracy of the prediction process.

title: Benford's Law and Deep Learning Autoencoders: An approach for Fraud Detection of Credit card Transactions in Social Media
abstract: Due to the internet and social media age, a huge volume of ecommerce sites are using online platform for their business transactions. The advantage of e commerce and online payable sites are faster service and time saving. Majority of the customers that use ecommerce sites carry out their transactions using credit cards. But the modern fraudsters are consuming the Internet platform to attempt fraud with ease. An attempt has been made to enlighten fraud in credit card domain using Benford's Law and deep learning Autoencoders algorithm in neural networks. To test the efficiency of our model, random forest binary classification models are used as a comparison. The model shows better results for our proposed model than existing solutions in terms of recall, precision and ROC AVC curve.

title: Internet Banking Fraud Detection Using Deep Learning Based on Decision Tree and Multilayer Perceptron
abstract: Fraud transactions have become a growing problem in the online banking sphere. As technology progresses, fraudsters also change their methods of committing fraud. There are also emerging technologies that allow fraudsters to mimic the transaction behavior of genuine customers and they also keep changing their methods so that it is difficult to detect fraud. This paper discusses the importance of fraud detection methods and compares Hidden Markov Model, Deep Learning, and Neural Network that are used to detect fraud in online banking transactions.

title: Financial statements fraud detection from unstructured data using deep learning
abstract: This research paper explores the application of the Universal Language Model Fine tuning (ULMFiT) technique, a novel deep transfer learning approach in the Natural Language Processing (NLP) field, to the financial statements fraud detection task. Additionally, the artifact investigated a simpler model, represented by a one dimensional Convolutional Neural Network (CNN) Both methods have been assessed with respect to the training time and predefined evaluation metrics. Overall, ULMFiT turned out to be considerably more computationally expensive to train and achieved an accuracy of 77% with F measure of 13% if used with a decision boundary of 0.5, and accuracy of 59% with F measure of 42% if calculated with a threshold of 0.2. In contrast, CNN model trained significantly faster and obtained the following metrics: accuracy of 82% with F measure of 42% for threshold 0.5, and accuracy of 83% with F measure of 58% for threshold 0.2. As a result, ULMFiT has been outperformed by one dimensional CNN on all examined metrics. The results are reported by using two different values for decision boundary due to the precision recall trade off, depending on the use case. In addition, this thesis investigated the impact of data preprocessing. The findings have shown that removing all numbers and special symbols with supplementary text truncation, limiting the sequence length, had a positive effect on both models, mentioned above.

title: Credit Card Fraud Detection using Deep Learning based on Auto Encoder and Restricted Boltzmann Machine
abstract: Frauds have no constant patterns. They always change their behavior; so, we need to use an unsupervised learning. Fraudsters learn about new technology that allows them to execute frauds through online transactions. Fraudsters assume the regular behavior of consumers, and fraud patterns change fast. So, fraud detection systems need to detect online transactions by using unsupervised learning, because some fraudsters commit frauds once through online mediums and then switch to other techniques. This paper aims to 1) focus on fraud cases that cannot be detected based on previous history or supervised learning, 2) create a model of deep Auto encoder and restricted Boltzmann machine (RBM) that can reconstruct normal transactions to find anomalies from normal patterns. The proposed deep learning based on auto encoder (AE) is an unsupervised learning algorithm that applies backpropagation by setting the inputs equal to the outputs. The RBM has two layers, the input layer (visible) and hidden layer. In this research, we use the Tensorflow library from Google to implement AE, RBM, and H2O by using deep learning. The results show the mean squared error, root mean squared error, and area under curve.

title: Fraud Detection using Machine Learning and Deep Learning
abstract: Frauds are known to be dynamic and have no patterns, hence they are not easy to identify. Fraudsters use recent technological advancements to their advantage. They somehow bypass security checks, leading to the loss of millions of dollars. Analyzing and detecting unusual activities using data mining techniques is one way of tracing fraudulent transactions. transactions. This paper aims to benchmark multiple machine learning methods such as k nearest neighbor (KNN) random forest and support vector machines (SVM) while the deep learning methods such as autoencoders, convolutional neural networks (CNN) restricted boltzmann machine (RBM) and deep belief networks (DBN) The datasets which will be used are the European (EU) Australian and German dataset. The Area Under the ROC Curve (AUC) Matthews Correlation Coefficient (MCC) and Cost of failure are the 3 evaluation metrics that would be used.