Plum Identity provides identity validation using behavioral analytics to prevent fraud at the application stage. The solution calculates a risk score based on numerous data points to detect synthetic identities, bots, and fraudulent organizations. This process validates identities without requiring Personally Identifiable Information (PII) or invasive checks.
Funding
Funding not disclosed
Founders
Product
Problem
Online fraud prevention often relies on Personally Identifiable Information (PII), creating privacy concerns and compliance challenges. Traditional methods can also be slow, cumbersome, and ineffective against sophisticated fraud techniques like synthetic identities and bot-driven attacks.
Solution
Plum Identity offers a real-time identity validation solution that leverages behavioral analytics to detect fraudulent activity without using PII. By analyzing tens of thousands of data points related to user behavior and device characteristics, Plum identifies stolen and synthetic identities, bots, drophouses, suspicious devices, and organized fraud networks prior to payment. The system calculates a risk score for each applicant interaction, enabling organizations to automatically approve, verify, or deny transactions based on pre-defined risk tolerances. Plum's technology can be implemented quickly, providing immediate fraud prevention without requiring extensive coding, training, or consulting services.
Target Audience
Plum Identity targets organizations that require effective fraud prevention while prioritizing user privacy and regulatory compliance, including financial institutions, e-commerce platforms, and government agencies.
Features
- Real-time behavioral analysis using thousands of data points to identify fraudulent patterns
- Detection of synthetic identities, drophouses, bots, and suspicious devices
- Risk scoring engine that assigns a composite risk score to each applicant interaction
- Automated decision-making based on configurable risk thresholds
- Secure hashing algorithms to scramble PII data within the user's computing environment
- Dashboard for viewing results, configuring models, and tuning thresholds
- Identification of bot-generated applications based on specific bot behaviors
- Detection of account takeovers by identifying hacked accounts redirecting payments