Collaborating Authors


Najm deploys SAS artificial intelligence and analytics solutions to combat fraud in insurance


SAS, the market leader in Analytics & Anti-fraud Technologies, and Najm for Insurance Services have announced a technology collaboration that will aim to bring SAS expertise to counter and reduce instances of fraud in Automobile and Motor insurance claims. Officials from both companies signed the agreement at a SAS event in Fairmont Riyadh on Wednesday. With the goal of streamlining claims through application assessment and taking a proactive approach to detect & deter fraud in the business, Najm is looking to improve efficiency in fraud identification, fast claims resettlement as well as the development of better-quality alerts, by utilizing the latest analytics & fraud detection technologies. Utilizing Artificial Intelligence and Machine-Learning technologies, SAS will automate aspects of Najm's claimant profiling, and will aim to complement existing manual processes to detect fraud claims through behavioral responses and automatically assess risk patterns. During the event, Najm CEO Dr. Mohammad Al-Suliman spoke about the partnership with SAS and the company's future plans.

Artificial Intelligence in Payment Processing – Current Applications Emerj


It seems that the majority of AI solutions for payment processing are focused on fraud detection and prevention. Some companies claim to offer straight-through processing software as well. We'll get started with background information about AI in payment processing, and then we'll explore the vendor use cases in depth individually. The companies discussed in this report vary in their densities of AI talent, which is one of the three rules of thumb we use when determining whether or not a company is actually leveraging AI or using it more for marketing purposes. We look for companies with AI talent in their C-suites first and foremost, but perhaps equally important is the number of data scientists employed at the company.

Deep Learning for Anomaly Detection: A Survey Machine Learning

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is twofold, firstly we present a structured and comprehensive overviewof research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art deep anomaly detection research techniques into different categories based on the underlying assumptions and approach adopted. Within each category, we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. Besides, for each category, we also present the advantages and limitations and discuss the computational complexity of the techniques inreal application domains. Finally, we outline open issues in research and challenges faced while adopting deep anomaly detection techniques for real-world problems.

Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization Machine Learning

Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated to the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an efficient fraud detection process. In this paper, we present a number of active learning strategies and we investigate their fraud detection accuracies. We compare different criteria (supervised, semi-supervised and unsupervised) to query unlabeled transactions. Finally, we highlight the existence of an exploitation/exploration trade-off for active learning in the context of fraud detection, which has so far been overlooked in the literature.