Every time you receive a call from your bank after making a purchase using your credit card, it's generally AI- powered systems running in the background assisting your bank with fraud detection. These calls -- along with push ads or SMS verifications are a form of two- factor authentication initiated to validate the identity of the person who has made the transaction. AI also has the power to identify strange or out of the ordinary purchase patterns and behaviors, which can also be used to warn banks whenever any potentially suspicious transaction is conducted at the client's end. Not just that, AI can also prioritize suspected fraudulent activity so that investigations can be on the base of urgency or significance. ML strategies which are developed by using the true data of consumers -- can remember the usual spending patterns of the clients so that whenever it spots an anomaly, it can raises a flag, thereby making the AI system more equipped for identifying fraud.

In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.

Let's start with a naive approach. You'll create a Random Forest model on the dataset and completely ignore the class imbalance. To start, you'll have to split the dataset into training and testing portions. Ideally, you want the percentage roughly the same in the train and test sets. Here's how to do the split and check the percentage of the positive class: Let's make it as simple as possible.

Fraud detection, one of the many cases of anomaly detection is an important aspect of financial markets. Is there any way to predict whether a transaction is fraudulent or not based on the history of transactions? Let's explore a neural network architecture as it attempts to predict the cases as frauds or not. By the end of this article, we'll be able to build an encoder-decoder architecture from scratch using Keras and classify the transactions as fraudulent or non-fraudulent. We use a dataset credit card fraud detection by the ULB machine learning group.

Economics, money, and the way we make payments have undergone several changes since the time of the Stone Age. In a sense all these are key indicators of our progress as a species. The primitive methods indicated our primitive way of living. Similarly, the current payment methods powered by cutting-edge technology boast our technological achievements of today. Digitization of payments was a huge jump towards the goal to achieve an easy, convenient, fast, and secure payment method.

With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security. In this work, we present a novel method for detecting fraud transactions by leveraging patterns from both users' static profiles and users' dynamic behaviors in a unified framework. To address and explore the information of users' behaviors in continuous time spaces, we propose to use \emph{time attention based recurrent layers} to embed the detailed information of the time interval, such as the durations of specific actions, time differences between different actions and sequential behavior patterns,etc., in the same latent space. We further combine the learned embeddings and users' static profiles altogether in a unified framework. Extensive experiments validate the effectiveness of our proposed methods over state-of-the-art methods on various evaluation metrics, especially on \emph{recall at top percent} which is an important metric for measuring the balance between service experiences and risk of potential losses.

Light GBM is a high-performance gradient boosting (GBDT, GBRT, GBM or MART) framework and is used for classification, machine learning, and ranking related tasks. Light GBM grows tree vertically while other algorithm grows trees horizontally. Light GBM grows tree leaf-wise while other algorithm grows level-wise. Leaf with max delta loss grows. By growing the same leaf, a leaf-wise algorithm can reduce more loss than a level-wise algorithm.

Real-time machine learning has access to a continuous flow of transactional data, but what it really needs in order to be effective is a continuous flow of labeled transactional data, and accurate labeling introduces latency. During this year, I heard and read a lot about real-time machine learning. People usually provide this appealing business scenario when discussing credit card fraud detection systems. They say that they can continuously update credit card fraud detection model in real-time (See "What is Apache Spark?", "…real-time use cases…" and "Real time machine learning"). It looks fantastic but not realistic to me.

During this year, I heard and read a lot about real-time machine learning. People usually provide this appealing business scenario when discussing credit card fraud detection systems. They say that they can continuously update credit card fraud detection model in real-time (See "What is Apache Spark?", "…real-time use cases…" and "Real time machine learning"). It looks fantastic but not realistic to me. One important detail is missing in this scenario – continuous flow of transactional data is not needed for model retraining.