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Your source for the latest fraud & AML intelligence, insights, research, and commentary.

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Most fraud and AML case management fails before the investigation even starts. Here's where the breakdown happens, and the step-by-step playbook high-performing teams use to fix it.

Why Fraud Case Management Breaks Down And the Playbook to Fix It

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A rabbit and a turtle in one green blazer representing Fraud & AML suddenly forced to work in one platform

Forrester Report: Financial Crime Is Converging And Most Fraud & AML Systems Aren’t Built for It

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This blog article provides a comprehensive overview of how agentic AI is transforming financial crime detection and operations, moving beyond simple chat interfaces to active execution.

Why Most Agentic AI Deployments Fail (and How to Get It Right)

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What is Claude Mythos? Learn how Anthropic’s latest AI model could reshape fraud, AML, and risk management for financial institutions.

Mythos Changes the Threat Model - The Fraud and AML Industry Needs to Catch Up.

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Explore biometric authentication risks, 2026 regulatory updates, and best practices for balancing security and privacy in financial services fraud prevention.

Top Biometric Authentication: Balancing Security and Privacy in Financial Services

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Compare the top 10 FRAML platforms of 2026. See how DataVisor, NICE Actimize, Quantexa & others rank on AI, real-time detection, and AML compliance.

Top 10 FRAML Platforms of 2026: Best Software Ranked & Compared

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Compare the top 10 AML platforms in 2026. Explore AI-powered detection, real-time monitoring, and how modern solutions reduce false positives.

Best AML Software in 2026 - Top 10 Platforms Ranked & Compared

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Financial indicators of human trafficking financial institutions should watch. Learn red flags, transaction patterns, and AML strategies to detect trafficking networks.
Digital Fraud Trends

Financial Indicators of Human Trafficking: What Financial Institutions Can Do

Digital Fraud Trends
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Account takeover fraud is draining life insurers' whole life policies with accumulated cash. Learn why rules-based systems are failing, and how AI is closing the gap in real time.
Digital Fraud Trends

Account Takeover in Life Insurance: How AI Is Protecting Policyholders

Digital Fraud Trends
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Digital Fraud Trends

5 Fraud Prevention Lessons You Can Learn from Online Marketplaces

Digital Fraud Trends
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Learn how AI-driven feature engineering is accelerating fraud detection with faster signal creation, continuous adaptation, standardizing a “5-minute strategy” for modern fraud prevention.
Digital Fraud Trends

The 5-Minute Fraud Strategy: How AI Is Redefining Feature Engineering for Fraud Detection

Digital Fraud Trends
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Explore the top three signals reshaping fraud and AML in 2026. This report breaks down the AI paradox, the reality of "agility debt" in FRAML convergence, and the impact of real-time payment velocity on risk decisioning.
Digital Fraud Trends

What’s reshaping fraud and AML in 2026? Three early signals from the Fraud & AML Executive Report

Digital Fraud Trends
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Stablecoins, Fraud, and the New Era of Financial Crime: What Every Bank and Fintech Needs to Know about bridges
Digital Fraud Trends

Stablecoins, Fraud, and the New Era of Financial Crime: What Every Bank and Fintech Needs to Know

Digital Fraud Trends
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Nacha is casting a wider net in 2026, expanding the scope of institutional liability to include authorized "False Pretenses" and Credit-Push fraud. Large green net
Digital Fraud Trends

Nacha’s 2026 Operating Rules: A Simple Guide to Mastering Compliance

Digital Fraud Trends
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The four types of risk organization structures diagram
Digital Fraud Trends

Fraud and Risk Organization Models: Pros, Cons, and Best Practices

Digital Fraud Trends
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AI Robot pointing at a green blackboard written on it AI Explainability vs Transparency
Featured

Regulatory Expectations Around Explainable AI

Featured
Case Studies
An array of credit cards in a a volatile market, where flexible risk platforms are key

How Macro Volatility Is Driving a Shift to Flexible Fraud & AML Platforms

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Team pieces together ensemble approach to AI
Featured

Why Rule-Based AML Is Failing & How an Ensemble AI Approach Fixes It

Featured
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Digital Fraud Trends

Top 10 Fraud Platforms for 2026: Plus, Evaluation Criteria, Challenges, and Trends

Digital Fraud Trends
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MoData + DataVisor partnership
Featured

DataVisor and MoData Partner to Accelerate Digital Fraud Prevention Across Africa

Featured
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Tony Kueh- Why I'm All In: Joining DataVisor as COO|Tony Kueh_COO_Why I'm All In_DataVisor
Quick Takes

Why I'm All In: Joining DataVisor as COO

Quick Takes
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Tackling the Complexities of Modern Payment Fraud, Yinglian Xie, CEO, DataVisor|
Digital Fraud Trends

Tackling the Complexities of Modern Payment Fraud: Key Insights from Fintech Meetup 2025

Digital Fraud Trends
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Wood blocks 2025
Quick Takes

Top Fraud Trends and Predictions for 2025 – And How Will the Industry Respond?

Quick Takes
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Fraud Prevention: What Fintechs Really Need from DataVisor. Fraud analyst overwhelmed by many screens
Quick Takes

Fraud Prevention: What Fintechs Really Need

Quick Takes
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FRAML done right: Fraud and AML teams tap shared date, but have separate workflows|FRAML Venn Diagram: Shared Dataset, Separate Workflows with different goals and needs
Quick Takes

FRAML Done Right: Shared Data, Not Workflows

Quick Takes
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Sponsor Banks and Fintechs collaborating to achieve global regulatory compliance
Quick Takes

7 Ways Sponsor Banks and Fintechs Are Collaborating to Achieve Regulatory Compliance in 2025

Quick Takes
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Unlocking Revenue Growth with Smart Fraud Prevention|Amanda Peterson, Director, Fraud and Risk, TabaPay|Mike Lavia, Director of Payments, DataVisor
Quick Takes

Unlock Revenue Potential with Smart Fraud Prevention

Quick Takes
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Digital Fraud Trends

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

Digital Fraud Trends
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Sponsor Banks' BaaS team working collaboratively across fraud and money laundering
Product Blogs

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

Product Blogs
Banking Fraud
fraud ring
Dummies Series

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

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

Compliance Solutions
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

Digital Fraud Trends
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fednow vs other payments|payment systems
Quick Takes

3 Ways FedNow is Different From Other Payments

Quick Takes
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Zigzagging toward APP Fraud Liability, Article by Mike Cook of DataVisor
Digital Fraud Trends

Zig-zagging Toward Compliance: The Uncertain Certainty of APP Fraud Liability

Digital Fraud Trends
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Mike Cook_VP Product Solutions_Datavisor
Quick Takes

Interview with Mike Cook, VP Product Solutions: 35 Years of Protecting People from Fraud

Quick Takes
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How to Choose the Right Fraud Detection Platform for E-Commerce|How E-Commerce Companies Choose the Right Fraud Detection Platform 1|How E-Commerce Companies Choose the Right Fraud Detection Platform|illustration of a man working on a laptop to secure his accounts
Quick Takes

How E-Commerce Companies Choose the Right Fraud Detection Platform

Quick Takes
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4 Types of Digital Bank Fraud_DataVisor_Graphic||fintech fraud|datavisor device intelligence impact. Reduced attack events within three weeks
Digital Fraud Trends

4 Major Digital Bank Frauds and How to Stop Them

Digital Fraud Trends
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first-party fraud|prevent payment fraud
Digital Fraud Trends

Fighting Authorized Payment Fraud: How to Stop Real-time Scams

Digital Fraud Trends
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Blog_3-ways-ot-use-AI-in-AML-Compliance_Datavisor
Product Blogs

3 Ways You Should be Using AI in Your AML Compliance

Product Blogs
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DataVisor 4 reasons to use uml for fraud detection|unspuervised machine learning datavisor|unsupervised machine learning
Product Blogs

4 Super Powers of Unsupervised Machine Learning

Product Blogs
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A complete guide to the proposed Regulation E changes, plus how Financial Institutions are preparing|Complete Guide to Regulation E Proposed Changes
CPO Corner

Complete Guide to Proposed Regulation E Changes

CPO Corner
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friendly fraud
Digital Fraud Trends

Strategies and Tools to Prevent Friendly Fraud in 2024

Digital Fraud Trends
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DEFEND day zero risk|
Quick Takes

Why Day Zero Risk Matters and Which Tools Achieve It

Quick Takes
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evolve bank and trust cease and desist order
Digital Fraud Trends

9 Important Lessons from Evolve Bank & Trust’s Cease and Desist

Digital Fraud Trends
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device intelligence|dEdge integration|DataVisor device intelligence
FraudTech

Why Device Intelligence is Critical for Fraud Fighters

FraudTech
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dennis maicon datavisor
Product Blogs

A Leader’s Guide to Navigating the Future of Fraud Prevention with DataVisor’s Dennis Maicon

Product Blogs
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check fraud 2024|check walker
Digital Fraud Trends

How to Defeat Rising Check Fraud in 2024

Digital Fraud Trends
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automated rule tuning|automated rule tuning false positive decrease detection accuracy increase|AI co-pilot ebook|generative AI|generative AI|comparative insights automated rule tuning|automated rule tuning ai co-pilot|ai co-pilot||AI co-pilot
FraudTech

How to Save 50% of Your Fraud Team’s Time with Automated Rule Tuning

FraudTech
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fraud prevention platform
Product Blogs

Can You Use the DataVisor Platform to Build Your Own Fraud Solution?

Product Blogs
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crypto scams
Digital Fraud Trends

Crypto Fraud Scams: A Deep Dive

Digital Fraud Trends
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BaaS regulatory requirements for sponsor banks and fintechs
Digital Fraud Trends

Regulatory Requirements for Sponsor Banks: What You Need to Know

Digital Fraud Trends
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romance scams
Digital Fraud Trends

How Romance Scams are Evolving and Ways to Stay Safe

Digital Fraud Trends
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Fraud trends 2023|
Digital Fraud Trends

Experts Name the Most Challenging Fraud Threats from 2023 to Watch in 2024

Digital Fraud Trends
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|changing fraud prevention provider
Quick Takes

7 Common Mistakes to Avoid When Migrating to a New Fraud Solution

Quick Takes
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Product Blogs

Introducing AI Co-pilot: Your New Generative AI-Powered Fraud-Fighting Companion

Product Blogs
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DataVisor CRO Kevin McWey|Kevin mcwey datavisor|Kevin McWey DataVisor|Kevin McWey DataVisor
Quick Takes

A Q&A with DataVisor’s new CRO Kevin McWey

Quick Takes
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Product Blogs

How DataVisor Provided NASA FCU with Award-winning Fraud Prevention

Product Blogs
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machine learning fraud detection|machine learning fraud prevention
CPO Corner

5 Things Every Fraud Leader Should Know About Machine Learning

CPO Corner
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Best fraud detection|types of machine learning fraud detection
Quick Takes

3 Key Ways Machine Learning Powers Fraud Prevention

Quick Takes
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FedNow
Digital Fraud Trends

FedNow: Everything You Need to Know

Digital Fraud Trends
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Digital fraud investigations|case management system fraud prevention|case management|fraud analyst|fraud analyst
Dummies Series

Starter's Guide to Digital Fraud Investigations

Dummies Series
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fraud analytics
Product Blogs

5 Signs It's Time for a New Fraud Solution

Product Blogs
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CPO Corner

AML Transaction Monitoring Explained: How to Catch a Money Launderer

CPO Corner
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digital fraud infographic
CPO Corner

Why Fake Accounts are a Starting Point for Fraud

CPO Corner
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Best fraud detection
CPO Corner

3 Tips to Enhance Fraud Detection While Minimizing Costs

CPO Corner
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illustration case management in fraud detection|digital fraud investigations handbook download banner
Dummies Series

Case Management Software - The Backbone of Your Digital Fraud Prevention Strategy

Dummies Series
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20211117_How to Stop Chargeback Fraud with AI-Powered Detection1200x630|20211117_How to Stop Chargeback Fraud with AI-Powered Detection600x400|DataVisor - Request Demo Banner|first-party fraud
Quick Takes

How to Prevent Policy Abuse Fraud and Friendly Fraud

Quick Takes
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Digital fraud investigations|A Closer Look at Digital Identities and Fraud (1)|Digital fraud investigations and CMS|Open Finance: The Future and Challenge of Data Sharing SOCIAL|Open Finance: The Future and Challenge of Data Sharing
Quick Takes

A Closer Look at Digital Identities and Fraud

Quick Takes
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What Finance Professionals Can Learn from Fraud History|Infamous Fraud Cases and Their Implications for Modern Fraud Experts|DataVisor - Digital Fraud Tracker Download Banner|illustration of three professionals having a discussion
Quick Takes

Infamous Fraud Cases and Their Implications for Modern Fraud Experts

Quick Takes
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Tradeoff Between Fraud Prevention and CX_1200x628|Fight Fraud in a Mobile-first World with Device Intelligence|Tradeoff Between Fraud Prevention and CX_Social|
Quick Takes

Does There Have to Be a Tradeoff Between Fraud Prevention and Customer Experience?

Quick Takes
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Demystifying Modern Fraud Attacks in Banking|Demystifying Modern Fraud Attacks in Banking|webinar banner modern fraud in banking
Quick Takes

Demystifying Modern Fraud Attacks in Banking

Quick Takes
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Fraud Detection Software in Financial Services- Build vs. Buy_1|To Build or to Buy. The ultimate software dilemma.|illustration depicting online shopping
Quick Takes

To Build or to Buy. The ultimate software dilemma.

Quick Takes
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3 Tips for Building Your Fraud Team|Meet the Next-Generation AML Solution|How to Build an Effective Fraud Team|teamwork illustration|build an effective fraud team download ebook banner
Quick Takes

3 Tips for Building Your Fraud Team

Quick Takes
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Food Delivery Fraud|Case Study: How One Company Used DataVisor to Combat Food Delivery Fraud During COVID-19
Quick Takes

Case Study: How One Company Used DataVisor to Combat Food Delivery Fraud During COVID-19

Quick Takes
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How to Use Identity Data and Behavior Intelligence for Fraud Detection_1|How to Use Identity Data and Behavior Intelligence for Fraud Detection|illustration of a man and woman securing their online presence
Quick Takes

How to Use Identity Data and Behavior Intelligence for Fraud Detection

Quick Takes
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ML + Linkage analysis_1|Linkage Analysis: A new approach to detecting and investigate AML and application fraud|illustration of a secured laptop|a knowledge graph|a knowledge graph
Quick Takes

Linkage Analysis: A New Approach to Detect and Investigate AML and Application Fraud

Quick Takes
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|How to Calculate the ROI of Transaction Fraud Prevention|fraud analytics|transaction fraud ROI preview|download transaction fraud roi calculator banner
Quick Takes

How to Calculate the ROI of Transaction Fraud Prevention

Quick Takes
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2021 Fraud Predictions 1|Contact Center Fraud: Key Trends and Challenges for 2021
Featured

Contact Center Fraud: Key Trends and Challenges for 2021

Featured
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|infographic on digital fraud protection
Digital Fraud Trends

Fraud Detection Now: A 2020 Retrospective

Digital Fraud Trends
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3 Capabilities Blog||
Product Blogs

3 Capabilities of Advanced Fraud Features that Help Your Fraud Team Adapt to Fast-Evolving Fraud

Product Blogs
Feature Platform
How to Find Emulators and Stop Mobile Fraud 1|Fight-Fraud-in-a-Mobile-first-World-with-Device-Intelligence|How to Find Emulators and Stop Mobile Fraud_1
Quick Takes

How to Find Emulators and Stop Mobile Fraud

Quick Takes
dEdge
linkage analysis|
Quick Takes

Linkage Analysis: How to Investigate Fraud Cases Beyond Table View

Quick Takes
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Exploring the CARTA approach|Exploring the CARTA approach|illustration depicting secure digital ecosystem
Quick Takes

Exploring the CARTA Approach to Fraud and Risk Management

Quick Takes
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rules engine
Quick Takes

How to make your rules engine more powerful by using advanced features

Quick Takes
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How Much Application Fraud Can Cost Banks 1_1200x|
Quick Takes

How Much Can Application Fraud Cost Banks? And a Bonus Downloadable ROI Calculator!

Quick Takes
Application Fraud
Fraud Detection
How AI Can Protect You from a Bot Attack||
Quick Takes

How AI Can Protect You from a Bot Attack

Quick Takes
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How to Find Money Mules|
Quick Takes

How to Find "Money Mules" with Machine Learning Fraud Detection Software

Quick Takes
Machine Learning
Money Mules
WEF DataVisor
Product Blogs

DataVisor Makes World Economic Forum’s 2020 List of Technology Pioneers

Product Blogs
AI
COVID-19
World Economic Forum
Gartner Cool Vendor 2020||Gartner Cool Vendor||Gartner Cool Vendor|Gartner Cool Vendor 2020
Product Blogs

DataVisor Is Named a Cool Vendor by Gartner

Product Blogs
Machine Learning
Unsupervised Machine Learning
5 Trend Fraud Manager|5 Trend Fraud Manager
Quick Takes

5 Trends Fraud Leaders are Talking about Right Now

Quick Takes
Fraud Management
Digital Fraud Trends

How COVID-19 Unemployment Scams Target PII

Digital Fraud Trends
Fraud Management
Digital Fraud Trends

How is the Financial Fraud Landscape Changing as the World Adapts to COVID-19?

Digital Fraud Trends
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||||||||||DataVisor - Shelter in Place
Digital Fraud Trends

Tracking the Impact of Social Distancing and Shelter-in-Place Policies on Fraud Trends and Threats

Digital Fraud Trends
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|DataVisor - Customer Experience and Fraud Prevention - Entire Customer Lifecycle|Data Visor Blog Header
Quick Takes

Balancing Customer Experience and Fraud Prevention Across the Entire Customer Lifecycle

Quick Takes
Fraud Prevention
Customer Experience
||DataVisor - Creating a Data-Centric Organization (1)
Technical Posts

Creating a Data-Centric Organization

Technical Posts
Data
Power Up Fraud Detection with DataVisors Advanced Rules Engine Cover Image|DataVisor Rules Engine|DataVisor Feature Platform - Rules Engine|DataVisor - Detection Rules - Rules Engine|Data Visor - Customized Actions - Rules Engine|Power Up Fraud Detection with DataVisors Advanced Rules Engine
Product Blogs

Power Up Fraud Detection with DataVisor’s Advanced Rules Engine

Product Blogs
Advanced Rules Engine
Rules-based Systems
Digital Fraud Trends

Buying Fake Twitter Followers Isn’t Just Bad Marketing. It’s Dangerous.

Digital Fraud Trends
Fake Accounts
Identity Theft
Exposing the Digital Footprints of Fraud - back|Exposing the Digital Footprints of Fraud
Quick Takes

Exposing the Digital Footprints of Fraud

Quick Takes
ATO
Research
Product Blogs

Automated Feature Engineering

Product Blogs
Machine Learning
|
Product Blogs

Bringing DataVisor to the Masses

Product Blogs
AI
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