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AI Approaches to Fraud Detection and Risk Management

AI Magazine

A false negative means that fraud, bad credit, or intrusion passes unnoticed, with potential loss of revenue or security. This workshop focused primarily papers, 10 of which were selected for with the Fourteenth National on what might loosely be termed presentation at the workshop. These Conference on Artificial Intelligence "improper behavior," which includes 10 papers were grouped into 3 categories. However, Glasgow applying classification techniques to were over 50 attendees, with a balanced does discuss the estimation of "inherent fraud and risk problems, including the mix of university and industry risk," which is the bread and butter use of clustering techniques to generate researchers. We sought participants data, highly skewed distributions ("improper Columbia University, and Phillip Chan to discuss and explore common behavior" occurs far less frequently of Florida Institute of Technology).

AI Approaches to Fraud Detection and Risk Management

AI Magazine

The 1997 AAAI Workshop on AI Approaches to Fraud Detection and Risk Management brought together over 50 researchers and practitioners to discuss problems of fraud detection, computer intrusion detection, and risk scoring. This article presents highlights, including discussions of problematic issues that are common to these application domains, and proposed solutions that apply a variety of AI techniques. There were over 50 attendees, with a balanced mix of university and industry researchers. The organizing committee consisted of Tom Fawcett and Foster Provost of Bell Atlantic Science and Technology, Ira Haimowitz of General Electric Corporate Research and Development, and Salvatore Stolfo of Columbia University. The purpose of the workshop was to gather researchers and practitioners working in the areas of risk management, fraud detection, and computer intrusion detection.

TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial Machine Learning

With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business. To tackle this problem, we introduce the TitAnt, a transaction fraud detection system deployed in Ant Financial, one of the largest Fintech companies in the world. The system is able to predict online real-time transaction fraud in mere milliseconds. We present the problem definition, feature extraction, detection methods, implementation and deployment of the system, as well as empirical effectiveness. Extensive experiments have been conducted on large real-world transaction data to show the effectiveness and the efficiency of the proposed system.

Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods Machine Learning

Credit card fraud is an ongoing problem for almost all industries in the world, and it raises millions of dollars to the global economy each year. Therefore, there is a number of research either completed or proceeding in order to detect these kinds of frauds in the industry. These researches generally use rule-based or novel artificial intelligence approaches to find eligible solutions. The ultimate goal of this paper is to summarize state-of-the-art approaches to fraud detection using artificial intelligence and machine learning techniques. While summarizing, we will categorize the common problems such as imbalanced dataset, real time working scenarios, and feature engineering challenges that almost all research works encounter, and identify general approaches to solve them. The imbalanced dataset problem occurs because the number of legitimate transactions is much higher than the fraudulent ones whereas applying the right feature engineering is substantial as the features obtained from the industries are limited, and applying feature engineering methods and reforming the dataset is crucial. Also, adapting the detection system to real time scenarios is a challenge since the number of credit card transactions in a limited time period is very high. In addition, we will discuss how evaluation metrics and machine learning methods differentiate among each research. NTRODUCTION The number of cashless transactions is at its peak point since the beginning of the digital era and it is most likely to increase in the future.

Uncovering Insurance Fraud Conspiracy with Network Learning Machine Learning

Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially fraudulent claims every day. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.