Synthesizing Credit Card Transactions
Two elements have been essential to AI's recent boom: (1) deep neural nets and the theory and practice behind them; and (2) cloud computing with its abundant labeled data and large computing resources. Abundant labeled data is available for key domains such as images, speech, natural language processing, and recommendation engines. However, there are many other domains where such data is not available, or access to it is highly restricted for privacy reasons, as with health and financial data. Even when abundant data is available, it is often not labeled. Doing such labeling is labor-intensive and non-scalable. As a result, to the best of our knowledge, key domains still lack labeled data or have at most toy data; or the synthetic data must have access to real data from which it can mimic new data. This paper outlines work to generate realistic synthetic data for an important domain: credit card transactions. Some challenges: there are many patterns and correlations in real purchases. There are millions of merchants and innumerable locations. Those merchants offer a wide variety of goods. Who shops where and when? How much do people pay? What is a realistic fraudulent transaction? We use a mixture of technical approaches and domain knowledge including mechanics of credit card processing, a broad set of consumer domains: electronics, clothing, hair styling, etc. Connecting everything is a virtual world. This paper outlines some of our key techniques and provides evidence that the data generated is indeed realistic. Beyond the scope of this paper: (1) use of our data to develop and train models to predict fraud; (2) coupling models and the synthetic dataset to assess performance in designing accelerators such as GPUs and TPUs.
Code (0)
등록된 구현이 없습니다.
Tasks
Cloud ComputingSimilar Papers 제목 키워드 기반
Solve fraud detection problem by using graph based learning methods
The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss of the credit cards' holders and the ban…
Fraud DetectionMultiple perspectives HMM-based feature engineering for credit card fraud detection
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactio…
Feature EngineeringFraud DetectionCredit Card Fraud Detection: A Deep Learning Approach
Credit card is one of the most extensive methods of instalment for both online and offline mode of payment for electronic transactions in recent times. credit cards invention has provided significant ease in electronic t…
Deep LearningFraud DetectionCredit card fraud detection using machine learning: A survey
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 it…
BIG-bench Machine LearningFeature EngineeringFraud DetectionSurveyTowards automated feature engineering for credit card fraud detection using multi-perspective HMMs
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactio…
Automated Feature EngineeringFeature EngineeringFraud DetectionMissing Values