Background of the problem
Many institutions continue to operate under rigid rules that generate friction, unnecessary blocks, and false positives, all of which affect the customer experience.
Fraud evolves every day.
The challenge is clear:
Value proposition
Smart security based on behavior and machine learning
We combine biometrics, multi-factor authentication, custom scoring, and predefined workflows to:
Core capabilities:
Comprehensive security platform
Combination of multi-factor authentication methods
Facial biometric assessment
Recognition of habitual devices
Integration with government agencies for identity validation
Integration with leaders in digital identity, AML, and fraud prevention
Brand protection and digital experience
Preserves institutional reputation
Reduces unnecessary friction
Creates secure and engaging digital experiences
Increases approvals with lower risk exposure
Automated threat management
Configuration of automatic warnings and blocks
Machine Learning for predictive detection
Reduction of manual intervention
Automation of control operations
Power user score
Make better decisions with a customized score built from advanced behavioral analysis algorithms and transactional profiles.
Protect your digital channels without slowing down your growth
Frequently asked questions
Does the solution have an adequate number of rules for effective fraud prevention?
Yes. The solution includes a wide range of predefined and customizable rules covering common scenarios and emerging threats. Additionally, it allows for adjusting variables or creating new rules according to the specific policies and needs of each institution.
How is it possible to track the activity and generate a proper follow-up of the operations?
The solution features an administrative portal where the entity can view alerts, configure rules, manage cases, and generate reports, providing full control over the prevention process.
Does this solution integrate with Bankingly's digital channels?
Sí. Se integra completamente con la plataforma de banca web y móvil, añadiendo una capa adicional de seguridad sobre los canales digitales existentes.
How does the solution behave regarding the management of potential fraud?
It can be configured to issue warnings or perform automatic blocks based on machine learning. The system identifies suspicious patterns and behaviors to generate preventive actions in real time.
What is the process for detecting potential fraud?
The process is divided into four stages:
Data collection: analysis of transactions, user behavior, and historical logs.
Behavioral analysis: identification of normal and anomalous patterns using advanced algorithms.
Anomaly detection: flagging of suspicious activities or unusual transactions.
Continuous improvements: constant updating of models and rules to adapt to new threats.
