Analyst Report
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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- 電子書 Public procurement integrity at risk
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- 電子書 Your journey to a GenAI future: A strategic path to success in banking
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- 分析報告 SAS is a Leader in The Forrester Wave™: AI Decisioning Platforms, Q2 2023.
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- 文章 6 ways big data analytics can improve insurance claims data processing
- 文章 Containing health care costs: Analytics paves the way to payment integrity
- 文章 Know your blind spots in tax fraud prevention
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- 分析報告 Chartis RiskTech Quadrant for Trade-Based AML Solutions 2022
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- 分析報告 SAS is a Leader in The Forrester Wave™: Anti-Money Laundering Solutions, Q3 2022
- 客戶案例 Fighting loan application fraud with cutting-edge analytics
- 客戶案例 European Banking-as-a-Service leader strengthens its AML/CFT and fraud surveillance system with SAS
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- 客戶案例 Stopping payment fraud in real time
- 電子書 Protecting the Payments
- 白皮書 Effective fraud analytics: 10 steps to detect and prevent insurance fraud
- 電子書 Faces of Fraud
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- 文章 When it matters: Safeguarding your organization from the inside
- 分析報告 IDC MarketScape: Worldwide Responsible Artificial Intelligence for Integrated Financial Crime Management Platforms 2022 Vendor Assessment
- 白皮書 Using Modern Analytics to Save Government Programs Millions
- 文章 5 steps to sustainable GDPR compliance
- 分析報告 Chartis RiskTech Quadrant for Enterprise Fraud Solutions, 2023: Vendor Analysis
- 白皮書 Managing Fraud Risk in the Digital Age
- 白皮書 Enforcing Tax Compliance in a Turbulent World
- 文章 Continuous monitoring: Stop procurement fraud, waste and abuse now
- 分析報告 Matrix: Leading Fraud & AML Machine Learning Platforms
- 分析報告 Matrix: Payment Integrity In Healthcare
- 白皮書 Machine Learning Use Cases in Financial Crimes
- 白皮書 Keeping Fraud Detection Software Aligned With the Latest Threats
- 文章 Analytics: A must-have tool for leading the fight on prescription and illicit drug addiction
- 文章 4 strategies that will change your approach to fraud detection
- 文章 Unemployment fraud meets analytics: Battle lines are clearly drawn
- 客戶案例 Advanced analytics and machine learning help Poste Italiane identify and stop fraud in real time while enhancing customer experience
- 白皮書 Fighting Insurance Application Fraud
- 文章 Analytics for prescription drug monitoring: How to better identify opioid abuse
- 白皮書 Data and Analytics to Combat the Opioid Epidemic
- 文章 Online fraud: Increased threats in a real-time world
- 白皮書 Leveraging Analytics to Combat Digital Fraud in Financial Organizations
- 白皮書 Proactive anti-financial crime strategies to improve compliance and reduce risk
- 白皮書 Achieving program integrity for health care cost containment
- 文章 How to prevent procurement fraud
- 白皮書 Government Procurement Offices
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- 客戶案例 A risk-based approach to combat money laundering in Israel
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- 白皮書 2021 State of Insurance Fraud Technology Study
- 白皮書 Detect and prevent digital banking fraud
- 文章 Detect and prevent banking application fraud
- 白皮書 Next-generation AML
- 文章 Are you covering who you think you’re covering?
- 分析報告 Celent: Insurance Fraud Detection Solutions: Health Insurance, 2022 Edition
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- 白皮書 Banking in 2035: global banking survey report
- 文章 Top prepaid card fraud scams
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- 白皮書 Banking in 2035: three possible futures
- 白皮書 AML Modernization
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- 白皮書 Fraudsters love digital
- 白皮書 Data, analytics and machine learning: The new frontier of fraud prevention
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- 白皮書 Top Trends: Why Tax Administrators Are Adopting New Data and Analytics Strategies
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- 白皮書 The Escalation of Digital Fraud
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- 白皮書 How Public Sector Agencies Can Use Analytics to Lead Through Crisis
- 白皮書 How AI and Machine Learning Are Redefining Anti-Money Laundering
- 白皮書 Managing the Intelligence Life Cycle
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- 白皮書 AI Is at the Forefront of Reducing Money Laundering and Combating the Financing of Terrorism
- 白皮書 Detect and Prevent Identity Theft
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- 文章 疫情期間網路交易大增,全面防護金融詐欺犯罪
- 客戶案例 Managing mergers and analytics: Ensuring reliable energy by eliminating risk
- 文章 The best gift you can give to thieves this holiday season? Your identity.
- 文章 Shut the front door on insurance application fraud!
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- 文章 Fraud detection and machine learning: What you need to know
- 文章 Health care cost containment through big data analytics
- 文章 Managing fraud risk: 10 trends you need to watch
- 客戶案例 Turkish insurer achieves real-time fraud detection
- 客戶案例 Preventing payment card fraud one transaction at a time
- 客戶案例 Advanced analytics can detect and prevent insurance fraud before losses occur
- 客戶案例 Fighting financial crime through a global anti-money laundering platform
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- 文章 Taking pre-emptive action to stem the tide of VAT fraud losses
- 文章 Mobile payments, smurfs and emerging threats
- 文章 Stop contract and procurement fraud
- 文章 Medicaid and benefit fraud in 2018 and beyond
- 文章 Applying technology to ensure voter integrity in elections
- 文章 Rethink customer due diligence
- 文章 Detecting health care claims fraud
- 文章 How to uncover common point of purchase
- 客戶案例 泰國銀行在即時管理詐欺偵測的同時也保護客戶
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- 客戶案例 洗錢防制
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