AI TOOL PROFILE
Paygilant Review: Fintech Fraud Prevention Software
- Security
- Cybersecurity
- Neo banks
- Challenger banks
- eWallets
- Crypto firms
- Financial institutions
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
At a glance
- Best for
- Neo banks, Challenger banks, eWallets, Crypto firms, Financial institutions
- Key use cases
- New Account Opening Fraud Detection, Account Takeover Prevention, Pre-Transaction Risk Assessment, SIM-Swap and Payment Fraud Monitoring
- Official website
- Visit paygilant official website
How AI is used
Paygilant is a fraud management system designed for the financial sector, including digital banks, crypto firms, and payment providers. The software focuses on identifying fraudulent activity in the pre-transaction stage by monitoring the user journey from login to logout.
The tool uses device fingerprinting, behavioral biometrics, and machine learning to assign risk scores at various checkpoints. This approach is designed to identify threats like account takeover and synthetic identity fraud without requiring frequent manual authentication steps.
Buyers should note that the tool is designed for mobile-first environments, such as apps and USSD.
Key Features
Device DNA Fingerprinting
Creates a unique ID for devices by analyzing attributes like model, OS, IP, and geolocation to identify suspicious hardware.
Behavioral Biometrics
Passively monitors touch time, finger velocity, scrolling pace, and typing patterns to identify the user.
Transaction Behavioral Maps
Uses machine learning algorithms to map a user's specific purchasing patterns and compare them against risk zones.
Multi-Checkpoint Risk Scoring
Assesses risk at different stages of the user journey, including registration, login, and adding payment methods.
App Interaction Monitoring
Analyzes how a user navigates the application to determine if the behavior is consistent with a legitimate user.
User Data Analysis
Analyzes on-device information, such as contact lists and call logs, to identify indicators of fraudulent account origination.
Use Cases
New Account Opening Fraud Detection
Analyzing device and user data during registration to help identify fake or stolen identities used to open mule accounts.
Account Takeover Prevention
Monitoring for unusual device attributes or behavioral changes that may suggest a fraudster has taken over an account.
Pre-Transaction Risk Assessment
Using behavioral maps and bio markers to score a transaction as risky or safe before funds are moved.
SIM-Swap and Payment Fraud Monitoring
Observing the user journey and device environment to help detect unauthorized access via SIM-swap or stolen payment methods.
FAQ
What does Paygilant do?
- Paygilant analyzes device attributes, user behavior, and transaction patterns to identify potential fraud before a transaction is completed.
Who is this software designed for?
- It is designed for the fintech ecosystem, including challenger banks, neo banks, eWallets, and crypto firms.
How does the software detect fraud without slowing down the user?
- It uses passive markers, such as touch velocity and typing patterns, to identify the user in the background.
How long does it take to implement Paygilant?
- The company states that the solution can be integrated within a matter of days.
Source category: Security
Source subcategory: Cybersecurity
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