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BioCatch Tech Stack

Behavioral biometrics platform protecting financial institutions from fraud

Computer and Network Security New York, NY 201–500 employees Founded 2011 Privately Held
biocatch.com LinkedIn

BioCatch detects financial crime by analyzing behavioral patterns—keystroke dynamics, mouse movement, touch activity, device attributes—collected across banking platforms. The stack (Python, Rust, Kafka, Snowflake, Databricks) reflects a data-heavy, streaming-first architecture built to process billions of sessions monthly. Active hiring in data, sales, and engineering with a senior-weighted mix signals expansion into new geographies and customer segments, while the project list reveals focus on mobile SDK integration and low-latency fraud-rule deployment.

Tech Stack 65 technologies

Core StackPython Rust Kafka Redis RabbitMQ PostgreSQL NetSuite Snowflake Delta Lake Iceberg Databricks PySpark AWS Kubernetes AWS CloudFront Nginx Prometheus Grafana Datadog Terraform JavaScript TypeScript React Azure Azure Kubernetes Service GCP Ubuntu CentOS Azure Front Door Coralogix+33 more
AdoptingReact Native Intune Android iOS WebView Jamf

What BioCatch Is Building

◆Challenges

  • Fight fraud
  • Combat fraud
  • High fraud detection rates
  • Scalability challenges
  • Browser compatibility issues
  • Web integration challenges
  • Monitoring cloud costs
  • Proof of value for pilots
  • Performance at scale
  • Cost at scale

▲Active Projects

  • Onboarding and implementing anti-fraud solutions across web and mobile applications
  • Deploy rules for acute fraud attacks
  • Systems implementation and integration projects
  • Deliver fraud detection performance reporting
  • Ai-driven development methodologies
  • Low-latency high-concurrency server components
  • High-availability architecture
  • Web sdk integration for enterprise customers
  • High-volume data processing infrastructure
  • Research emerging threats in fraud use cases

Hiring Activity

Accelerating20 roles · 15 in 30d

Department

Data
5
Sales
5
Engineering
4
Ops
3
Support
3
Security
1

Seniority

Senior
10
Mid
6
Director
2
Junior
1
Lead
1
Manager
1

Notable leadership hires: Sales Account Director

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About BioCatch

BioCatch is a fraud-prevention platform for financial institutions that uses behavioral biometrics and device intelligence to distinguish legitimate users from attackers. The company collects over 3,000 anonymized data points per user session—behavioral, device, and contextual signals—and runs machine-learning models to flag anomalies in real time. Founded in 2011 and headquartered in New York, BioCatch serves 287 financial institutions globally, analyzing 16 billion user sessions monthly across web and mobile banking channels. The product architecture spans onboarding, continuous authentication, rules deployment, and performance reporting, with engineering efforts concentrated on scalability, mobile integration, and cost optimization at high volume.

HeadquartersNew York, NY
Company Size201–500 employees
Founded2011
Hiring MarketsUnited States, Israel, India, United Kingdom, Germany, Saudi Arabia, France

Frequently Asked Questions

What technology does BioCatch use for fraud detection?

Python, Rust, Kafka, Snowflake, Databricks, and PySpark underpin the platform. Behavioral data flows through Kafka, lands in Snowflake, and powers ML models on Databricks. Real-time serving uses low-latency Rust components and Redis caching.

What is BioCatch working on in 2024?

Current projects include mobile SDK integration for web and app platforms, low-latency fraud-rule deployment systems, high-volume data processing infrastructure, and detection of emerging threat vectors like RATs and malware behavior.

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How this profile is built

BioCatch's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.