Fraud rings often operate beneath the surface of financial systems, executing small, seemingly unrelated transactions across multiple accounts and identities. Traditional systems, which analyze transactions in isolation, often miss the bigger pattern.

The Challenge:

• Fraud networks use low-signal, high-volume activity to evade detection

• Traditional tools lack graph logic or relationship modeling

• Connections across devices, times, and accounts often go undetected

• Manual graph analysis is too slow for real-time prevention

Quantum-Inspired Solution

This use case shows how quantum-inspired graph modeling and optimization helps uncover interconnected fraud structures, saving organizations millions and protecting user trust.

Graph-Based Behavioral Mapping

HessQ creates dynamic graphs from transaction data — identifying suspicious clusters, loops, and flows across accounts

Uses Travelling Salesman Problem (TSP)-inspired logic and QUBO optimization to reconstruct fraud routes, even when spread over time

Continuously updates and flags new high-risk nodes and paths, ensuring early intervention

TSP & QUBO for Fraud Path Optimization

Real-Time Network Pattern Detection

The Results:

• +55% more fraudulent links identified

• Detection time reduced from 2 days to under 10 minutes

• Patterns previously considered "noise" are now meaningful signals

• Enables fraud ring dismantling before losses escalate