SAS IS BEST IN CLASS
Matrix: Leading Fraud & AML Machine Learning Platforms
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
- E-book
- White Paper
- White Paper
- ARTICOLO
- Blog Post
- Estratto
- Case Study
- Infografica
- Intervista
- Ricerca
- Serie
- Video
- Webinar
- Testimonianza
-
ARTICOLO
4 strategies that will change your approach to fraud detectionAs fraudulent activity grows and fighting fraud becomes more costly, financial institutions are turning to anti-fraud technology to build better arsenals for fraud detection. Discover four ways to improve your organization's risk posture.
-
ANALYST REPORT
IDC MarketScape: Worldwide Responsible Artificial Intelligence for Integrated Financial Crime Management Platforms 2022 Vendor AssessmentLearn why SAS is positioned in the Leaders category in the 2022 IDC MarketScape for worldwide responsible artificial intelligence for integrated financial crime management platforms.
-
ARTICOLO
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.
-
ANALYST REPORT
Matrix: Leading Fraud & AML Machine Learning PlatformsSAS is a best-in-class vendor in the most recent Datos Insights report, Matrix: Leading Fraud & AML Machine Learning Platforms.
-
ARTICOLO
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.
-
Testimonianza
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.
-
ARTICOLO
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.
-
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.
-
ARTICOLO
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.
-
ARTICOLO
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 Datos Insights 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.