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1. Fraud Detection 정의 Fraud Detection(혹은 Fraud Detection System)이란 금융, 전자상거래, 통신, 보험 등 다양한 산업 영역에서 발생할 수 있는 사기나 부정 거래를 조기에 식별하고 방지하기 위한 기술적·분석적 접근을 의미합니다. 부정 행위 탐지의 핵심은 ‘정상적인 패턴과의 미묘한 차이’를 찾아내는 것입니다. 이를 위해 대량의 거래 데이터, 사용자 행동 로그, 네트워크 활동
Data is growing faster than most organizations’ ability to manage it. At the same time, business leaders are under pressure to deliver insights quickly and cost‑effectively. Traditional, closed systems often make that harder: they lock data into proprietary formats, increase duplication and limit flexibility. That’s why open data architecture is
La gestión de riesgos ha evolucionado drásticamente. En un panorama marcado por la volatilidad económica, la sofisticación del fraude y los cambios regulatorios acelerados, las organizaciones que abordan el riesgo únicamente como un desafío se encuentran en clara desventaja. La analítica de riesgos avanzada, se ha convertido en la herramienta
Discover how to establish a foundation for success with AI in insurance.
The holiday season can be joyful, exciting, exhausting and stressful! Add hosting a large meal on top of that and it can get overwhelming. Following a few guidelines and some hosting tips can help reduce that overwhelmed feeling. Planning ahead, outsourcing where you can, and delegating can help make the
At SAS, we use and contribute to a wide range of open source projects. This series – SAS Contributes – highlights how our teams give back to the open source community. In this installment, we’re focusing on OpenSearch. If you’ve ever sifted through thousands of system logs or product reviews to
En 2024, près d’une entreprise française sur deux a subi une cyberattaque majeure, et dans 60 % des cas, tout a commencé par un simple mail ou appel frauduleux. Derrière chaque clic, chaque voix clonée, l’ingénierie sociale exploite nos failles humaines pour contourner les défenses les plus sophistiquées. Phishing, deepfake, usurpation
This is the fourth in a five-part blog post series that delves into modernizing marketing strategies through the integration of advanced marketing platforms. I had the pleasure of interviewing Shaun Memon of Munvo for this series, where we discuss a wide range of topics, from fragmented MarTech stacks, first-party data
One of the lesser talked-about issues large organizations face is the siloes that their various business units operate in. It’s a peculiar situation born from the specificity of tasks and objectives assigned to hyper-specialized teams within an enterprise. This separation of tasks inevitably leads to critical dependencies between teams. However,
It is difficult to evaluate high-dimensional integrals. One numerical technique that can be useful is quasi-Monte Carlo integration. In this article, I show how you can generate quasirandom points in SAS and use them to evaluate a definite integral on a compact region. For simplicity, the example in this article
If you’re building AI solutions today, you already know the stakes: faster decisions, trusted results and the ability to scale without blowing up your compute budget. What you may not always see is what’s happening under the hood to make that possible. That’s where strong technology partnerships matter. For more
Digital twin technology generates diverse, accurately labeled synthetic datasets in virtual environments, enabling faster, safer, and more reliable AI model development for PPE detection.
I had the privilege of joining the Dean’s Speaker Series at UNC Kenan-Flagler Business School, hosted in partnership with the Kenan Institute of Private Enterprise. My sincere thanks to Dean Mary Margaret Frank, the Kenan Institute team and all who made this event possible. It was an honor to share
No one is immune to identity theft – I know this firsthand. I was targeted while working at a financial institution where I was also a credit card customer. One day at work, I received an internal call from the bank’s customer contact center. I was informed that someone impersonating
Managing workloads in modern analytics environments is not keeping systems running, it’s about making sure the right jobs get the right resources at the right time. As organizations move analytics to the cloud, powered by Kubernetes, balancing workloads across computer resources becomes a critical challenge.