Quantum-Inspired Credit Risk & Fraud Engine
Q-Risk-Engine
Quantum-Inspired Credit Risk & Fraud Engine
- Availability
- In development
Overview
Q-Risk-Engine is a hybrid quantum-inspired risk platform for institutional-grade credit risk scoring, fraud detection, and portfolio optimization. Built across four planes - credit, fraud, optimization, and governance - with 8 core modules and proto-defined service contracts. Delivers TypeScript, Python, and Java SDKs for integration into existing risk infrastructure. The engine applies quantum-inspired optimization algorithms to credit scoring models, enabling faster convergence on risk surfaces that classical solvers struggle with at scale. Fraud detection leverages anomaly detection with graph-based relationship modeling across transaction networks. The governance plane enforces policy, audit trails, and regulatory reporting across all risk decisions.
What exists in the repository
pnpm workspaces · Turborepo
- MVP implementation completed and documented
- Full product tree mapped in-repo
Repository state as of its latest mounted snapshot. Not an availability claim.
- 4
- Risk Planes
- 8
- Core Modules
- 3 languages
- SDKs
- Quantum-inspired
- Approach
- Proto-defined
- Contracts
- Development
- Stage
Stack capabilities
What Q-Risk-Engine delivers
- Hybrid quantum-inspired credit risk scoring with classical fallback for production reliability
- Fraud detection with graph-based transaction network analysis and anomaly scoring
- Portfolio optimization using quantum-inspired solvers for large-scale risk surfaces
- Governance plane with policy enforcement, audit trails, and regulatory reporting
- 4 planes: credit, fraud, optimization, governance - 8 core modules
- Proto-defined service contracts for institutional integration and interoperability
- TypeScript, Python, and Java SDKs for multi-language enterprise deployment
- Real-time risk scoring with sub-100ms latency targets for transaction decisioning
Interfaces and modules
Integration surfaces
- Credit Risk API
- Fraud Detection API
- Optimization SDK
- Governance API
Deployment
Where it runs
On-premises, VPC, or hybrid for regulated financial institutions
Third-Party Services & Dependencies
HEOSSI solutions integrate with and depend on third-party services including blockchain networks, cloud infrastructure providers, cryptographic libraries, identity providers, and certificate authorities.
HEOSSI is not responsible for:
- Availability, performance, or security of third-party services
- Changes to third-party APIs, protocols, or standards
- Third-party service outages, breaches, or failures
- Costs associated with third-party services
- Compliance of third-party services with applicable laws
Performance metrics and capabilities may be affected by third-party service limitations. Customers are responsible for evaluating and accepting risks associated with third-party dependencies.
FAQ
Common questions about Q-Risk-Engine
What risk capabilities does Q-Risk-Engine provide?
Q-Risk-Engine provides four planes: credit risk scoring, fraud detection, portfolio optimization, and governance. It includes 8 modules with TypeScript, Python, and Java SDKs, proto-defined service contracts, and institutional-grade risk infrastructure using hybrid quantum-inspired algorithms.
What latency does Q-Risk-Engine target for transaction decisioning?
Q-Risk-Engine targets sub-100ms latency for real-time transaction decisioning. Quantum-inspired solvers run alongside a classical fallback path so production reliability is preserved even when the quantum-inspired pipeline degrades or is unavailable.
How does Q-Risk-Engine integrate with existing risk infrastructure?
Q-Risk-Engine ships TypeScript, Python, and Java SDKs against proto-defined service contracts, so credit, fraud, optimization, and governance APIs slot into existing institutional pipelines. Fraud detection adds graph-based transaction-network analysis with anomaly scoring, and the governance plane enforces policy, audit trails, and regulatory reporting across every decision.
Next step
Interested in Q-Risk-Engine?
Discuss deployment options, technical evaluations, and structured pilots with the HEOSSI engineering team.