Blog V2

Digital Fraud Trends

Fraud Awards 2024: The Most Shocking and Sophisticated Schemes of the Year

Sponsor Banks' BaaS team working collaboratively across fraud and money laundering

Why Banking-as-a-Service Sponsor Banks Are Unifying Fraud and AML

fraud ring

Fraud Rings: How They Work and Why Uncovering Them is a Challenge

Future Proof Customer Onboarding to be Fraud Resistant_Insights from Brian Hughes_DataVisor Blog|Future-Proof Fraud-Resistant Customer Onboarding_6 Core Capabilities_DataVisor Blog|10 ESSENTIAL SIGNALS Detecting and Mitigating Fraud Risk Signals Bot Detection Behavioral Analytics Device Intelligence Phone & Email Stolen ID Synthetic ID First Party Linkage Analysis Document Verification Government Sources|STRATEGY & GOVERNANCE  The Foundation of Effective Fraud Prevention Key Components COMMON PITFALLS Strategy &  Governance Benchmarking capabilities against industry best practices Cross-functional collaboration Shared scorecard for fraud metrics Alignment of fraud prevention with customer experience Waiting for large losses to justify fraud prevention investments Siloed approach to fraud management Lack of a unified strategy across departments Implementation Tips BEST PRACTICES Establish regular cross-functional meetings to manage the new account funnel Develop a comprehensive fraud risk management program Continuously assess and update fraud prevention strategies Ensure clear communication of fraud prevention goals across the organization Justify fraud investments based on capability benchmarks, not just loss reduction Involve marketing, fraud tech, and operations teams in managing the onboarding process Use a common scorecard with metrics for fraud rates, customer friction, and costs Recognize that great customer experience is backed by strong fraud capabilities|RAPID RESPONSE & CONTINUOUS OPTIMIZATION KEY COMPONENTS Response &  Improvement Constant testing of signal providers Regular threshold adjustments 24-hour rule implementation Quarterly/monthly model updates Attack playbook preparation Fraud red team exercises Monitor early warning signals Implement new rules within 24 hours of vulnerability detection Maintain an up-to-date attack response plan Use AI-powered models for faster adaptation Conduct regular fraud defense simulations|ORCHESTRATIONConnecting Data for Effective Fraud Prevention KEY FEATURES KEY BENEFITS BEST PRACTICES Orchestration Streamlines access to diverse data sources Facilitates testing of new signal providers Offers insights for updating and expanding fraud strategies Connects to multiple signal providers through a single integration Simplifies contracting with resale agreements Provides strategic advice based on industry experience Regularly test new signal providers Leverage orchestrator's expertise for signal selection Utilize both third-party and internal data for comprehensive fraud prevention|DECISIONINGEvolving from Rules to AI-Powered Models AI MODELS RULES OPTIMAL SOLUTION Decisioning Improved scalability Enhanced accuracy Greater adaptability to new fraud patterns + Enforce non-negotiable policies (e.g., blocking known fraudsters) Serve as a secondary line of defense = Reduced manual management of complex rule sets Faster adaptation to evolving fraud tactics More efficient handling of multiple variables in onboarding|INVESTIGATION Swift Action on Fraud Alerts INVESTIGATION Swift Action on Fraud Alerts KEY FEATURES GOAL & IMPORTANCE BEST PRACTICES KEY FEATURES GOAL & IMPORTANCE BEST PRACTICES Investigation Rapid case connection through linkage analysis Threshold-based prioritization of losses Integration with decisioning platform for insights Identify vulnerabilities and implement fixes within 24 hours Fraud losses indicate system vulnerabilities Quick investigation enables rapid response and prevention Use linkage analysis to group related cases Focus on largest losses first Cross-train staff for surge capacity Leverage decisioning insights for efficient investigation Rapid case connection through linkage analysis Threshold-based prioritization of losses Integration with decisioning platform for insights Identify vulnerabilities and implement fixes within 24 hours Fraud losses indicate system vulnerabilities Quick investigation enables rapid response and prevention Use linkage analysis to group related cases Focus on largest losses first Cross-train staff for surge capacity Leverage decisioning insights for efficient investigation

Digital Fraud Trends

Future-Proofing Customer Onboarding to Prevent Fraud: Insights from Brian Hughes

DataVisor’s Knowledge Graph: The Holy Grail for Financial Fraud Investigations

Buy Now Pay Later (BNPL) Fraud_picture of mobile bnpl purchase_DataVisor|buy now pay later scam|How to Detect and Prevent BNPL Fraud Harness Comprehensive Data Sources Leverage third-party data connectors Implement device intelligence and behavioral biometrics Use Real-Time Data Orchestration Centralize information for 360° customer views Enable continuous transaction monitoring Customize Rules and Features Tailor fraud detection to your business needs Quickly develop and test new features Utilize Unsupervised Machine Learning Capture emerging fraud patterns in real-time Stay ahead of evolving threats Leverage Generative AI Automate tedious tasks Boost efficiency with AI-assisted rule tuning|

Digital Fraud Trends

Buy Now Pay Later (BNPL): The Rising E-commerce Payment Choice and Its Fraud Concerns

Is Vendor Consolidation the Right Approach? Understand the Benefits and Risks. Q& A with Yinglian Xie, CEO & Cofounder, DataVisor

Vendor Consolidation vs Orchestration: Understand the Benefits and Risks with DataVisor's Co-founder and CEO, Yinglian Xie

fednow vs other payments|payment systems

3 Ways FedNow is Different From Other Payments

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