The technology behind HAWK:AI Development principles to power AML/CFT and Compliance
Development principles powering AML and Compliance
HAWK:AI consistently delivers top-quality software with quick turnaround times using agile development principles and the highest degree of automation.
In summary – development principles for AML:
- Infrastructure as Code (IaC) for predictable, repeatable 1-click deployments, anywhere
- Test-driven development producing consistent quality code, continuously testing on unit, component, and end-to-end system level. Tests also include user interface, performance, and security testing.
- Machine learning lifecycle and pipeline for trusted, reproducible, and understood machine learning models
- Microservice architecture for highest system resiliency and redundancy to keep customer uptime a priority
- Multi-level monitoring and alerting from single hardware and software components, service to platform, and business KPI levels
Infrastructure as Code (IaC) for the fastest deployment possible
Quick, predictable deployment is vital for HAWK:AI to stay cloud-agnostic and rapidly expand into new regions or setups.
IaC dramatically shortens the software development lifecycle, accelerating the deployment of our AML solutions for your teams. The benefits of this approach allow HAWK:AI to port to new cloud providers and dedicated setups based on the latest proven technology like TerraForm, Kubernetes/Helm/Kustomize, or Argo CD. The result: infrastructure is established within days and full implementation for enterprise-level customers completed in just 4 months.
Benefits of HAWK:AI’s Infrastructure as Code approach
- Short implementation time thanks to rapid provisioning and deployment
- Rapid scaling and fully-realized benefits of cloud architecture, yet stay cloud agnostic
- Fast, simple deployment of backups and disaster recovery
- Dramatically reduce human error in the deployment process, particularly when rapid reaction is needed
Test-Driven Development (TDD) for a low-maintenance, high impact product
HAWK:AI uses Test-Driven Development (TDD) to continuously expand our platform with consistently high quality and speed. This development principle allows us to focus on product quality upfront, reducing potential bugs and ensuring new features are not held hostage by technical debt.
Our testing is:
- Multi-level: tests are created and constantly expanded on unit, component, and end-to-end system-level and monitoring its code coverage.
- Including all system aspects: tests also cover user interface, performance, and security testing
- Run on all code: testing backend, frontend, machine learning and infrastructure code
- Embedded in continuous integration: any code changes trigger test runs continuously
- Backed by processes: 4-eyes principle built into development cycle through code review as a mandatory step
Benefits of HAWK:AI’s Test-Driven Development approach
- Expedited timelines for customer feedback to become new product features
- Continuous deployment allows us to stick to tight deadlines without jeopardizing quality
- Key focus on software quality means fewer bugs, fewer surprises, and happier customers
Microservice architecture for efficiency, resiliency, and redundancy
HAWK:AI focuses on bringing innovation to market in a rapid, yet reliable way. To do this, we use a microservice architecture using battle-proven technologies like Kubernetes and Kafka.
Benefits of HAWK:AI’s Microservice Architecture
- Small codebase over monolithic application allows for rapid deployment of new features
- Scalability of subsystems allows efficient capacity customization for your needs
- No customer impact during feature updates or bug fixes for uninterrupted service delivery
- Highest resiliency and able to self-heal and recover from component to subsystem failures
We're always happy to discuss the technology powering the future of AML and Fraud Compliance
Monitor any transaction for red flags using a comprehensive set of rules in combination with Behavioral Analytics.
Screen customers against Sanctions, PEP, watchlists, and adverse media during onboarding and thereafter.
Customer Risk Rating
Dynamically score customer risk using internal and external data. Add behavioral analytics for richer context.
Screen counterparties against Sanctions and Country lists in real-time. Cleanse data and tune name-matching.