SAS is a Leader in The Forrester Wave™: Enterprise Fraud Management, Q2 2024
As noted in the Forrester evaluation, "SAS provides a broad selection of out-of-the-box EFM machine learning models. The vendor's EFM vision and product roadmap are first-rate and include not only transaction monitoring but also cyber (online) fraud management."
SAS’s productized, out-of-the-box supervised and unsupervised machine learning models for banking transaction monitoring are ahead of the competition and require less effort to train than those of competitors. Analyst investigation screens are intuitive and customizable. Reporting is versatile, visually pleasing, and easy to configure."
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백서
Generative AI in Health Care: Opportunities and Cautions -
고객 사례
Advanced analytics and machine learning help Poste Italiane identify and stop fraud in real time while enhancing customer experience -
고객 사례
Protecting policyholders through better fraud analysis -
기사
6 ways big data analytics can improve insurance claims data processing -
고객 사례
사회복지 사기 적발로 수백만 달러를 절약하고 시민의 신뢰 강화 -
기사
Containing health care costs: Analytics paves the way to payment integrity -
기사
Know your blind spots in tax fraud prevention -
고객 사례
Fast analytical defense -
기사
Putting an end to pay and chase -
고객 사례
Fighting loan application fraud with cutting-edge analytics -
고객 사례
European Banking-as-a-Service leader strengthens its AML/CFT and fraud surveillance system with SAS -
고객 사례
Combating financial crime and terrorism financing with real-time sanctions screening -
고객 사례
Stopping payment fraud in real time -
분석 보고서
Chartis RiskTech Quadrant for Trade-Based AML Solutions 2022 -
분석 보고서
Chartis RiskTech100 2024 -
고객 사례
Managing mergers and analytics: Ensuring reliable energy by eliminating risk -
분석 보고서
SAS is a Leader in The Forrester Wave™: AI Decisioning Platforms, Q2 2023. -
분석 보고서
SAS is a Leader in The Forrester Wave™: Anti-Money Laundering Solutions, Q3 2022 -
백서
Using Digital Identity To Unleash Organizational Data -
백서
Fighting Money Laundering with Intelligent Automation -
E-BOOK
Protecting the Payments -
백서
Effective fraud analytics: 10 steps to detect and prevent insurance fraud -
E-BOOK
Faces of Fraud -
백서
Protect the Integrity of the Procurement Function -
기사
When it matters: Safeguarding your organization from the inside -
분석 보고서
IDC MarketScape: Worldwide Responsible Artificial Intelligence for Integrated Financial Crime Management Platforms 2022 Vendor Assessment -
백서
Using Modern Analytics to Save Government Programs Millions -
기사
5 steps to sustainable GDPR compliance -
분석 보고서
Chartis RiskTech Quadrant for Enterprise Fraud Solutions, 2023: Vendor Analysis -
백서
Managing Fraud Risk in the Digital Age -
백서
Enforcing Tax Compliance in a Turbulent World -
기사
Continuous monitoring: Stop procurement fraud, waste and abuse now -
분석 보고서
Matrix: Leading Fraud & AML Machine Learning Platforms -
분석 보고서
Matrix: Payment Integrity In Healthcare -
백서
Machine Learning Use Cases in Financial Crimes -
백서
Keeping Fraud Detection Software Aligned With the Latest Threats -
기사
Analytics: A must-have tool for leading the fight on prescription and illicit drug addiction -
기사
4 strategies that will change your approach to fraud detection -
기사
Unemployment fraud meets analytics: Battle lines are clearly drawn -
백서
Fighting Insurance Application Fraud -
고객 사례
자금세탁 방지 활동을 지원하는 분석 기술 -
기사
Analytics for prescription drug monitoring: How to better identify opioid abuse -
백서
Data and Analytics to Combat the Opioid Epidemic -
기사
Online fraud: Increased threats in a real-time world -
백서
Leveraging Analytics to Combat Digital Fraud in Financial Organizations -
백서
Proactive anti-financial crime strategies to improve compliance and reduce risk -
백서
Achieving program integrity for health care cost containment -
기사
How to prevent procurement fraud -
백서
Government Procurement Offices -
백서
Fraud in Communications -
고객 사례
자금세탁 범죄에 맞서는 이스라엘의 위험 기반 전략 -
백서
Rethinking customer due diligence -
백서
2021 State of Insurance Fraud Technology Study -
백서
Detect and prevent digital banking fraud -
기사
Detect and prevent banking application fraud -
백서
Next-generation AML -
기사
Are you covering who you think you’re covering? -
고객 사례
Financial lender cuts third-party fraud by more than 80% with layered detection -
분석 보고서
Celent: Insurance Fraud Detection Solutions: Health Insurance, 2022 Edition -
분석 보고서
Celent Insurance Fraud Detection Solutions: Property and Casualty Insurance, 2022 Edition -
백서
Banking in 2035: global banking survey report -
기사
Top prepaid card fraud scams -
기사
Improve child welfare through analytics -
백서
Procurement integrity powered by continuous monitoring -
백서
Banking in 2035: three possible futures -
백서
AML Modernization -
E-BOOK
The Future of Energy & Utilities: Transform Through Innovation -
백서
Data-Driven Performance -
백서
Fighting the Rising Tide of Medicaid Fraud -
백서
Developing Trust: Uniting Fraud and Consumer Experience Through Digital Identity -
백서
Fraudsters love digital -
백서
Data, analytics and machine learning: The new frontier of fraud prevention -
백서
Anti-Fraud Technology -
백서
Value and Opportunity: An Executive Guide to Procurement Integrity -
E-BOOK
Fight money laundering with these 5 next-gen game changers from SAS -
E-BOOK
High velocity decisions. Trusted outcomes. -
백서
Top Trends: Why Tax Administrators Are Adopting New Data and Analytics Strategies -
분석 보고서
백서
What Lies Beneath -
백서
Payments Without Borders -
백서
The Escalation of Digital Fraud -
백서
Safer communities, trusted law enforcement -
백서
Advanced Analytics For Dissolving Data Silos -
백서
How Public Sector Agencies Can Use Analytics to Lead Through Crisis -
백서
How AI and Machine Learning Are Redefining Anti-Money Laundering -
백서
Managing the Intelligence Life Cycle -
백서
Balancing Fraud Detection and the Customer Experience -
백서
AI Is at the Forefront of Reducing Money Laundering and Combating the Financing of Terrorism -
백서
Detect and Prevent Identity Theft -
기사
Payment fraud evolves fast – can we stay ahead? -
기사
The best gift you can give to thieves this holiday season? Your identity. -
기사
Shut the front door on insurance application fraud! -
기사
Online payment fraud stops here -
기사
Strengthen your payment fraud defenses with stronger authentication -
기사
Fraud detection and machine learning: What you need to know -
기사
Health care cost containment through big data analytics -
기사
Managing fraud risk: 10 trends you need to watch -
고객 사례
Turkish insurer achieves real-time fraud detection -
고객 사례
Fighting financial crime through a global anti-money laundering platform -
고객 사례
Preventing payment card fraud one transaction at a time -
고객 사례
Advanced analytics can detect and prevent insurance fraud before losses occur -
고객 사례 fluvius uses sas procurement cockpit
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기사
Taking pre-emptive action to stem the tide of VAT fraud losses -
기사
Mobile payments, smurfs and emerging threats -
기사
Stop contract and procurement fraud -
기사
Medicaid and benefit fraud in 2018 and beyond -
기사
Applying technology to ensure voter integrity in elections -
기사
Rethink customer due diligence -
기사
Detecting health care claims fraud -
기사
How to uncover common point of purchase -
기사
Uncover hidden financial crime risk -
기사
How AI and advanced analytics are impacting the financial services industry -
기사
What do drones, AI and proactive policing have in common? -
고객 사례
북유럽 120개 은행의 범죄 예방 및 규정 준수 -
기사
Next generation anti-money laundering: robotics, semantic analysis and AI -
기사
How can analytics change the world of 'Narcos'? -
기사
Small-time cheats and organized crime: Benefits fraud re-examined -
기사
Tackling the new terrorist threat -
기사
Proactive detection – A new approach to counter terror -
기사
20/20 vision of risk
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