Transactional Security & Behavioral Modeling

We build systems that move beyond rules-based fraud checks. Our models learn subtle anomalies in behavior, providing high-accuracy scoring while drastically reducing false positives.

01

Sub-Millisecond Fraud Scoring

Implementing high-speed classification models optimized for low latency, allowing instant decision-making during payment processing or login events.

02

Network-Based Fraud Detection

Utilizing graph databases and graph neural networks to uncover complex, interconnected fraud rings and non-obvious relationships between accounts.

03

Model Drift & Self-Correction

Designing MLOps pipelines to monitor fraud patterns, automatically retrain models on new attack vectors, and adapt to emerging threats in real time.

04

High-Precision Anomaly Modeling

Techniques focused on optimizing the balance between precision and recall, minimizing legitimate transactions flagged as fraudulent, improving customer experience.

Core AI Engine for Anomaly Detection

Delivering a high-performance fraud detection system relies on combining custom Machine Learning algorithms with strong implementation and operational control.

01

Custom ML Model Development

Architecting and training specialized models (e.g., deep learning autoencoders or isolation forests) for identifying subtle, complex anomalies in large financial datasets.

Custom AI Model Development & Integration

02

Generative AI for Alert Reporting

Integrating LLMs to summarize complex fraud events, create narrative reports for compliance teams, and assist analysts in case management.

Generative AI Development

03

Rigorous Project & Product Management

Applying disciplined management and security protocols to ensure the fraud detection system is deployed robustly and governed correctly within regulated environments.

Project & Product Management