Topics Fraud Defenses Anti-money Laundering (AML): Rules for Catching Financial Crime Crowdsourced Abuse Reporting Device Fingerprinting Email Reputation Service IP Reputation Service SR 11-7 Compliance Supervised Machine Learning Two-Factor Authentication (2FA) Unsupervised Machine Learning Fraud Tactics Bot Attacks Call Center Scams Card Cloning Credential Stuffing Data Breaches Device Emulators GPS Spoofing Money Mule Scams P2P VPN Networks Phishing Attacks SIM Swap Fraud URL Shortener Spam Web Scraping Fraud Tech Device Intelligence Feature Engineering Identity (ID) Graphing Fraud Types App Install Fraud Application Fraud Bust-Out Fraud Buyer-Seller Collusion Content Abuse Loan Stacking Synthetic Identity Theft Types of Bank Frauds 12 Most Common Types of Bank Frauds Account Takeover (ATO) Fraud Check Fraud ACH Fraud What is First-Party Fraud and How to Prevent It Wire Fraud: What It Is, Examples, and How to Stop It Zelle Fraud: The Rapidly Rising Real-Time Scam Types of Card Fraud Credit card fraud Debit Card Fraud How to stop lost or stolen card fraud Feature Engineering Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. High-quality features are important for fraud detection, because a machine learning model is only as good as the data and features it is fed. Today, data scientists spend 70% of their time on data cleaning and feature engineering. DataVisor makes the feature engineering process a lot easier by offering an advanced and automated Feature Platform. Why Is DataVisor’s Feature Platform Unique? Out-of-the-Box Feature Packages for Fraud Use Cases DataVisor’s Feature Platform provides pre-built feature packages that are optimized for specific fraud types, including application fraud, ATO, transaction fraud and other scenarios. Extensive out-of-the-box features based on DataVisor’s domain expertise reduce time spent manually building features from scratch. Deep Learning Features and the Global Intelligence Network Deep learning helps detect script-generated content, disposable email services, anonymous proxies, suspicious IP usage and more. Gather intelligence from DataVisor’s consortium database to provide unique signals such as entity ages, geo-usage profiling and blacklists. Backtesting on Historical Data and Rapid Deployment Easily backtest on extensive historical data to validate detection performance, with full flexibility to choose the data’s timestamp and sample set. Results are returned within seconds; all the features are production-ready and can be deployed in minutes. High Scalability and Big Data Architecture Handle years of data for large institutions and process massive amounts of data in real time — 10,000+ QPS with only 10-50 ms latency. To minimize computation costs, DataVisor builds features on top of features, so that each feature is computed only once. Download the Product Sheet