AI TOOL PROFILE
DataVisor: AI Fraud Detection and AML Platform
- Security
- Fraud Detection
- Enterprise companies
- Financial services companies
- Banks
- Credit unions
- Fintechs
- Digital payment providers
- Life insurance companies
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
At a glance
- Best for
- Enterprise companies, Financial services companies, Banks, Credit unions, Fintechs
- Key use cases
- Account Onboarding Protection, Payment Fraud Prevention, Account Takeover (ATO) Prevention, Loan and Application Risk Assessment, Promotions and Policy Abuse Monitoring
- Integrations
- AWS, GCP, Azure, AliCloud
- Official website
- Visit datavisor official website
How AI is used
DataVisor is a fraud and anti-money laundering (AML) platform designed for financial services, including banks, credit unions, and fintechs. The system uses unsupervised machine learning and AI agents to help identify known and emerging fraud patterns.
The platform supports various stages of the customer lifecycle, from initial onboarding and application risk assessment to monitoring card, ACH, and wire payments. It also includes tools for case management and supports automated regulatory reporting to help with compliance tasks.
Buyers should note that this tool is built for high-scale environments, designed to process thousands of queries per second with low latency. Due to its enterprise focus, it is primarily suited for organizations with dedicated risk and compliance teams.
Prospective users should confirm how the platform's data ingestion process aligns with their existing data architecture and whether the automated reporting features meet their specific regional regulatory requirements.
Key Features
Unsupervised Machine Learning
Helps identify new and emerging fraud patterns that may be missed by traditional rules-based systems.
Real-Time Transaction Monitoring
Monitors ACH, wire, card, loan, and check transactions as they happen to detect suspicious activity.
Knowledge Graph Linking
Connects devices, behaviors, and entities to help uncover coordinated fraud rings.
Behavioral Biometrics and Device Intelligence
Analyzes device fingerprinting and user behavior to help detect account takeovers and risky devices.
Automated SAR/CTR Reporting
Supports the generation of Suspicious Activity Reports and Currency Transaction Reports for regulatory compliance.
Case Management Console
Provides a workspace for analysts to investigate alerts, prioritize cases, and conduct group-level reviews.
Use Cases
Account Onboarding Protection
Supports the verification of new customers while identifying synthetic identities and fraudulent sign-ups.
Payment Fraud Prevention
Monitors real-time card and ACH/wire transfers to help identify money-laundering patterns and unauthorized debits.
Account Takeover (ATO) Prevention
Correlates suspicious logins and behavioral anomalies to detect unauthorized account access.
Loan and Application Risk Assessment
Analyzes applications for credits and loans to spot manipulated or fake entries.
Promotions and Policy Abuse Monitoring
Helps detect duplicate accounts and organized rings attempting to abuse referral or incentive programs.
Integrations
- AWS
- GCP
- Azure
- AliCloud
FAQ
Who is DataVisor designed for?
- DataVisor is designed for enterprise-level organizations, specifically those in financial services such as banks, credit unions, fintechs, and digital payment providers.
How long does it take to integrate DataVisor?
- The vendor states that the integration process typically takes less than two weeks.
Does DataVisor collect personally identifiable information (PII)?
- The company states that it does not collect PII data, as it uses unsupervised machine learning to process non-PII data.
Where can DataVisor be deployed?
- It supports deployment on-premises, in private clouds, or via major cloud providers including AWS, GCP, Azure, and AliCloud.
Source category: Security
Source subcategory: Fraud Detection
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