As digital wallets handle millions of real-time transactions, each involving multiple risk factors (location, device, behavior, time, amount), traditional models often oversimplify or miss complex interactions between them.

The Challenge:

• Traditional systems often evaluate risk signals in isolation

• Difficult to detect fraud when risk factors are subtle but correlated

• Manual rule tuning is slow and not scalable

• Hard to balance fraud prevention vs. user friction

Quantum-Inspired Solution

Quantum-inspired optimization provides a powerful way to analyze and balance multiple risk signals simultaneously, improving the accuracy and efficiency of fraud detection and transaction approval.

Multi-Dimensional Risk Modeling

HessQ turns each transaction into a risk vector combining time, device, location, transaction history, and behavioral patterns

Using a Quantum-Inspired Binary Optimization (QUBO) engine, HessQ identifies the optimal decision path across all signals to minimize both fraud risk and false declines

The model continuously adapts, scoring new transaction patterns as they emerge, ensuring instant approvals or alerts based on contextual risk

QUBO-Based Global Optimization

Real-Time Risk Scoring

The Results:

• Up to 70% reduction in false positives

• Risk scores calculated in under 1 second

• Detects complex fraud even when each factor seems normal

• Adaptive to user behavior and evolving threat patterns