DataVisor

Analyzes incoming transactions in real-time using machine learning models to identify fraudulent activity across payment channels, lending platforms, and account opening flows. Provides immediate decisioning without waiting for batch processing or manual review.

Analyzes user behavior patterns including typing speed, mouse movements, navigation patterns, and interaction sequences to establish baseline user profiles and detect anomalous behavior indicative of account takeover or impersonation.

Provides tools for fraud analysts to investigate flagged cases, review evidence, collaborate on case resolution, and manage investigation workflows from alert to closure.

Enables creation and management of custom fraud detection rules and policies that can be adjusted based on business needs, fraud trends, and operational requirements without requiring code changes or model retraining.

Generates comprehensive reports and dashboards on fraud trends, detection performance, loss metrics, and operational KPIs to support fraud strategy, executive reporting, and regulatory compliance.

Provides REST APIs and integration points that allow fraud detection decisions to be embedded directly into transaction processing systems, enabling real-time fraud blocking at the point of transaction.

Creates unique device profiles based on hardware characteristics, software configuration, browser properties, and network attributes to identify and track devices across sessions and channels, enabling detection of multi-account fraud and device-based attacks.

Correlates fraud signals and patterns across multiple business channels including payments, lending, and account opening to identify coordinated fraud schemes and prevent fraud leakage across different product lines.

Identifies novel and previously unseen fraud patterns using unsupervised machine learning and anomaly detection techniques, enabling detection of new fraud tactics without waiting for historical labeled examples or manual rule creation.

Generates comprehensive risk scores for transactions, users, and accounts by combining multiple data signals and model outputs into a single interpretable score that prioritizes fraud cases by severity and likelihood.

Detects fraudulent account creation attempts by analyzing application data, identity verification signals, and behavioral patterns during the account opening process to prevent synthetic identity fraud and account takeover at enrollment.

Identifies fraudulent loan applications and disbursement schemes by analyzing application data, borrower behavior, collateral information, and loan characteristics to prevent loan fraud and reduce credit losses.

Analyzes payment transactions in real-time to detect unauthorized payments, card fraud, money laundering, and payment scheme abuse across credit cards, ACH transfers, wire transfers, and other payment methods.

Continuously monitors fraud detection model performance metrics and detects model degradation or data drift that could reduce fraud detection effectiveness, enabling proactive model retraining and system adjustments.