How it Works?
Using Machine learning (ML) to protect and secure, digital transactions at scale.
Tackle Fraud Threats
Account takeover, Merchant Fraud, Delinquency, Bustoffs, New account fraud, Mule accounts, common points of compromise, remote application takeover and a growing list of new vulnerabilities need to be mitigated while ensuring a great customer experience.
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Minimize Losses
Identify fraudulent and delinquent behaviour at a transaction level, to facilitate early mitigation. Quicker response and escalation will result in lower losses and better engagement with customers.
Reduce False Positives
The key to tuning machine learning models is to balance customer experience with the likelihood of loss. Determining the threshold for your business to balance true positives vs false negatives and false positives is key. We deliver accurate results and also avoid blocking & friction for genuine customers.
Problem Statement
Account Takeover
Uncover Account Takeovers And Fraudulent Activities In Real-Time Using Alternate Data And ML Models.
Social Engineering Scams
Analyse User Patterns And Data To Identify Even Subtle Deviations From The User Behaviour And Get Alerts In Real-Time.
Increase Operational Efficiency
Case management, escalation matrix, whitelists, blacklists, step-up process, auto-retry, watchlists, false positives, and ROCs are all part of the framework that is needed if you want to go beyond implementation to improvement and master the game against fraudsters.
Cloud Agnostic Deployment
Our real-time analytics platform offers flexible deployment options including; on-premise, cloud, SaaS or hybrid. Work with our sales team to identify the right solution to meet your regulatory and business needs.
Battle Fraud with Accuracy and Scalability
Payments digitization is underway, across the board and across verticals, with banks and fintechs accentuating the change. Protect digital transactions from frauds using machine learning at scale to deliver the accuracy expected by customers.