SAS provides a cohesive, open, elastic, and scalable platform that enables high-performing advanced analytics, machine learning model development and deployment, and real-time decisioning across a diverse set of fraud and AML use cases. The SAS platform supports the end-to-end data mining process and empowers a simple, powerful, and automated engine to handle tasks across the analytics life cycle from data engineering to model development, visualization, and deployment, to ongoing monitoring and optimization.
Explore More SAS Resources
To browse resources by type, select an option below.
- Select resource type
- Analyst Report
- White Paper
- White Paper
- Blog Post
- Book Excerpt
- Case Study
- Customer Story
- Analyst Report Aite Matrix: Leading Fraud & AML Machine Learning PlatformsSAS is a best-in-class vendor in the most recent AiteNovarica report, Aite Matrix: Leading Fraud & AML Machine Learning Platforms.
- Customer Story Financial lender cuts third-party fraud by more than 80% with layered detectionAxcess Financial uses SAS Identity 360 to dramatically reduce fraud losses and boost customer satisfaction.
- Customer Story Pharmacy benefit manager slashes fraud, waste and abuse using artificial intelligencePrime Therapeutics saves its clients $355 million in 18 months with AI-powered SAS Detection and Investigation for Health Care.
- Customer Story Brazil tackles insurance fraud with AI and analyticsCNseg relies on SAS to thwart fraud, improves alert accuracy by 67%.
- Customer Story Advanced analytics can detect and prevent insurance fraud before losses occurYdrogios Insurance limits damage, reduces costs and shields its competitive advantage with SAS® Detection and Investigation for Insurance.
- Article Analytics: A must-have tool for leading the fight on prescription and illicit drug addictionStates and MFCUs now have the analytics tools they need to change the trajectory of the opioid crisis by analyzing data and predicting trouble spots – whether in patients, prescribers, distributors or manufacturers. The OIG Toolkit with free SAS® programming code makes that possible.
- Article Shut the front door on insurance application fraud!Fraudsters love the ease of plying their trade over digital channels. Smart insurance companies are using data from those channels (device fingerprint, IP address, geolocation, etc.) coupled with analytics and machine learning to detect insurance application fraud perpetrated by agents, customers and fraud rings.
- Article Detect and prevent banking application fraudCredit fraud often starts with a falsified application. That’s why it’s important to use analytics starting at the entrance point. Learn how analytics and machine learning can detect fraud at the point of application by recognizing the biggest challenge – synthetic identities.
- White Paper Machine Learning Use Cases in Financial CrimesLearn 10 proven ways machine learning can boost the efficiency and effectiveness of fraud and financial crimes teams – from data collection to detection to investigation and reporting.
- Article Super-charge your fraud detection techniquesPreview a white paper by the International Institute for Analytics on how to evolve the fraud function from a set of fraud detection techniques to a cohesive, continuously improving system.
- Article Fraud detection and machine learning: What you need to knowMachine learning and fraud analytics are critical components of a fraud detection toolkit. Here’s what you’ll need to get started – from integrating supervised and unsupervised machine learning in operations to maintaining customer service while defending against fraud.
- Article When it matters: Safeguarding your organization from the insideWith evolving threats, fraud detection technologies have to be flexible and nimble, and automated risk detection is a crucial component of decision advantage.
©2021 Aite-Novarica Group. All rights reserved. Reproduction of this report by any means is strictly prohibited. Photocopying or electronic distribution of this document or any of its contents without prior written consent of the publisher violates U.S. copyright law.